Dynamic Neural Networks: A Survey

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

Squeeze-and-Excitation Networks

Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667 · see citing works

CBAM: Convolutional Block Attention Module

Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853 · see citing works

Robust Real-Time Face Detection

Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097 · see citing works

Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex

Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680 · see citing works

Non-local Neural Networks

Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596 · cited 1× · see citing works

Learning Deep Features for Discriminative Localization

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428 · see citing works

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., & Hu, Q. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01155 · Cited by 9,641 · cited 1× · see citing works

Learning Spatiotemporal Features with 3D Convolutional Networks

Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593 · see citing works

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

Carreira, J., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.502 · Cited by 9,570 · see citing works

Deformable Convolutional Networks

Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.89 · Cited by 7,513 · see citing works

Adaptive Mixtures of Local Experts

Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991) · Neural Computation · doi:10.1162/neco.1991.3.1.79 · Cited by 5,384 · see citing works

Simple model of spiking neurons

Izhikevich, E. M. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820440 · Cited by 4,828 · see citing works

Residual Attention Network for Image Classification

Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.683 · Cited by 4,073 · see citing works

AutoAugment: Learning Augmentation Strategies From Data

Cubuk, E. D., Zoph, B., Mané, D., Vasudevan, V., & Le, Q. V. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00020 · Cited by 4,026 · see citing works

Transformers in Vision: A Survey

Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022) · ACM Computing Surveys · doi:10.1145/3505244 · Cited by 3,595 · see citing works

Neural network-based face detection

Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554 · see citing works

Networks of spiking neurons: The third generation of neural network models

Maass, W. (1997) · Neural Networks · doi:10.1016/s0893-6080(97)00011-7 · Cited by 3,551 · see citing works

On calibration of modern neural networks

Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · arXiv · doi:10.48550/arxiv.1706.04599 · Cited by 3,435 · see citing works

Selective Kernel Networks

Li, X., Wang, W., Hu, X., & Yang, J. J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00060 · Cited by 3,416 · see citing works

KPConv: Flexible and Deformable Convolution for Point Clouds

Thomas, H., Qi, C. R., Deschaud, J., Marcotegui, B., Goulette, F., & Guibas, L. J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00651 · Cited by 3,400 · see citing works

Semantic Image Synthesis With Spatially-Adaptive Normalization

Park, T., Liu, M.-Y., Wang, T.-C., & Zhu, J.-Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00244 · Cited by 2,974 · see citing works

Deformable ConvNets V2: More Deformable, Better Results

Zhu, X., Hu, H., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00953 · Cited by 2,943 · see citing works

Learning Efficient Convolutional Networks through Network Slimming

Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., & Zhang, C. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.298 · Cited by 2,761 · see citing works

Multimodal Unsupervised Image-to-Image Translation

Huang, X., Liu, M.-Y., Belongie, S., & Kautz, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_11 · Cited by 2,597 · see citing works

Measurement Reliability and Validity

(2000) · Research Methods in Applied Settings · doi:10.4324/9781410605337-29 · Cited by 2,541 · see citing works

Film: Visual reasoning with a general conditioning layer

Perez, E., Strub, F., De Vries, H., Dumoulin, V., & Courville, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11671 · Cited by 2,490 · see citing works

Democracy Barometer. Methodology. Version 4.

Merkel, W., Bochsler, D., Bousbah, K., Bühlmann, M., Giebler, H., Hänni, M., Heyne, L., Müller, L., Ruth, S., & Weßels, B. (2004) · Intellectum (Universidad de La Sabana) · doi:10.1007/s13398-014-0173-7.2 · Cited by 2,331 · see citing works

SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning

Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.667 · Cited by 2,073 · see citing works

Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Bengio, Y., Léonard, N., & Courville, A. (2013) · arXiv · doi:10.48550/arxiv.1308.3432 · Cited by 1,910 · see citing works

Dynamic Convolution: Attention Over Convolution Kernels

Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., & Liu, Z. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01104 · Cited by 1,582 · see citing works

Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition

Fu, J., Zheng, H., & Mei, T. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.476 · Cited by 1,459 · see citing works

Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph

Bagher Zadeh, A., Liang, P. P., Poria, S., Cambria, E., & Morency, L.-P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1208 · Cited by 1,433 · see citing works

Deep Convolutional Network Cascade for Facial Point Detection

Sun, Y. J., Wang, X., & Tang, X. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.446 · Cited by 1,405 · see citing works

A convolutional neural network cascade for face detection

Li, H., Lin, Z., Shen, X., Brandt, J., & Hua, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299170 · Cited by 1,390 · see citing works

Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs

Simonovsky, M., & Nikos Komodakis, A. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.11 · Cited by 1,346 · see citing works

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts

Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., & Ed, H. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220007 · Cited by 1,329 · see citing works

BranchyNet: Fast inference via early exiting from deep neural networks

Teerapittayanon, S., McDanel, B., & Kung, H. T. (2016) · 2016 23rd International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr.2016.7900006 · Cited by 1,326 · see citing works

PointRend: Image Segmentation As Rendering

Kirillov, A., Wu, Y., He, K., & Girshick, R. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00982 · Cited by 1,244 · see citing works

VideoBERT: A Joint Model for Video and Language Representation Learning

Sun, C., Myers, A., Vondrick, C., Murphy, K. P., & Schmid, C. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00756 · Cited by 1,212 · see citing works

CARAFE: Content-Aware ReAssembly of FEatures

Wang, J., Chen, K., Xu, R., Liu, Z., Loy, C. C., & Lin, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00310 · Cited by 1,178 · see citing works

Speeding up Convolutional Neural Networks with Low Rank Expansions

Jaderberg, M., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.88 · Cited by 1,164 · see citing works

Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition

Zheng, H., Fu, J., Mei, T., & Luo, J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.557 · Cited by 1,045 · see citing works

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Fedus, W., Zoph, B., & Shazeer, N. (2021) · arXiv · doi:10.48550/arxiv.2101.03961 · Cited by 1,005 · see citing works

HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition

Yan, Z., Zhang, H., Piramuthu, R., Jagadeesh, V., DeCoste, D., Di, W., & Yu, Y. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.314 · Cited by 936 · cited 2× · see citing works

CondenseNet: An Efficient DenseNet Using Learned Group Convolutions

Huang, G., Liu, S., Maaten, L. V. D., & Weinberger, K. Q. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00291 · Cited by 904 · see citing works

AutoInt

Song, W., Shi, C., Xiao, Z., Duan, Z., Xu, Y., Zhang, M., & Tang, J. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357925 · Cited by 869 · see citing works

Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks

Roy, A. G., Navab, N., & Wachinger, C. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00928-1_48 · Cited by 869 · see citing works

Selectivity for the shape, size, and orientation of objects for grasping in neurons of monkey parietal area AIP.

Murata, A., Gallese, V., Luppino, G., Kaseda, M., & Sakata, H. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.83.5.2580 · Cited by 841 · see citing works

An Attentive Survey of Attention Models

Chaudhari, S., Mithal, V., Polatkan, G., & Ramanath, R. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3465055 · Cited by 819 · see citing works

The application of two-level attention models in deep convolutional neural network for fine-grained image classification

Xiao, T., Xu, Y., Yang, K., Zhang, J., Peng, Y., & Zhang, Z. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298685 · Cited by 807 · see citing works

Video Frame Interpolation via Adaptive Separable Convolution

Niklaus, S., Mai, L., & Liu, F. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.37 · Cited by 746 · see citing works

Integrating Multimodal Information in Large Pretrained Transformers

Rahman, W., Hasan, M. K., Lee, S., Zadeh, A. B., Mao, C., Morency, L., & Hoque, M. E. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.214 · Cited by 743 · see citing works

An Empirical Study of Spatial Attention Mechanisms in Deep Networks

Zhu, X., Cheng, D., Zhang, Z., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00679 · Cited by 659 · see citing works

SkipNet: Learning Dynamic Routing in Convolutional Networks

Wang, X., Yu, F., Dou, Z.-Y., Darrell, T., & Gonzalez, J. E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_25 · Cited by 643 · see citing works

Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation

Jo, Y., Oh, S. W., Kang, J., & Kim, S. J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00340 · Cited by 619 · see citing works

Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

Yang, F., Choi, W., & Lin, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.234 · Cited by 608 · see citing works

End-to-End Learning of Action Detection from Frame Glimpses in Videos

Yeung, S., Russakovsky, O., Mori, G., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.293 · Cited by 585 · see citing works

Meta-SR: A Magnification-Arbitrary Network for Super-Resolution

Hu, X., Mu, H., Zhang, X., Wang, Z., Tan, T., & Sun, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00167 · Cited by 545 · see citing works

Deep Neural Decision Forests

Kontschieder, P., Fiterau, M., Criminisi, A., & Bulò, S. R. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.172 · Cited by 519 · see citing works

Video Frame Interpolation via Adaptive Convolution

Niklaus, S., Mai, L., & Liu, F. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.244 · Cited by 515 · see citing works

SpotTune: Transfer Learning Through Adaptive Fine-Tuning

Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T., & Feris, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00494 · Cited by 506 · see citing works

Session-Based Social Recommendation via Dynamic Graph Attention Networks

Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., & Tang, J. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3290989 · Cited by 496 · see citing works

BlockDrop: Dynamic Inference Paths in Residual Networks

Wu, Z., Nagarajan, T., Kumar, A., Rennie, S., Davis, L. S., Grauman, K., & Feris, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00919 · Cited by 465 · see citing works

DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

Rao, Y., Zhao, W., Liu, B., Lu, J., Zhou, J., & Hsieh, C.-J. (2021) · arXiv · doi:10.48550/arxiv.2106.02034 · Cited by 448 · see citing works

Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem

Hein, M., Andriushchenko, M., & Bitterwolf, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00013 · Cited by 444 · see citing works

Neural Aggregation Network for Video Face Recognition

Yang, J., Ren, P., Zhang, D., Chen, D., Wen, F., Li, H., & Hua, G. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.554 · Cited by 411 · see citing works

Attention-Based Multimodal Fusion for Video Description

Hori, C., Hori, T., Lee, T.-Y., Zhang, Z., Harsham, B., Hershey, J. R., Marks, T. K., & Sumi, K. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.450 · Cited by 401 · see citing works

SEAN: Image Synthesis With Semantic Region-Adaptive Normalization

Zhu, P., Abdal, R., Qin, Y., & Wonka, P. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00515 · Cited by 396 · see citing works

Hierarchical Multiscale Recurrent Neural Networks

Chung, J., Ahn, S., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.01704 · Cited by 386 · see citing works

Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition

Tang, Y.-S., Tian, Y., Lu, J.-W., Li, P.-Y., & Zhou, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00558 · Cited by 383 · see citing works

Convolutional Networks with Adaptive Inference Graphs

Veit, A., & Belongie, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_1 · Cited by 374 · see citing works

SRM: A Style-Based Recalibration Module for Convolutional Neural Networks

Lee, H., Kim, H., & Nam, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00194 · Cited by 374 · see citing works

Modulating early visual processing by languagePreprint

de Vries, H., Strub, F., Mary, J., Larochelle, H., Pietquin, O., & Courville, A. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00683 · Cited by 368 · see citing works

Squeeze-and-Attention Networks for Semantic Segmentation

Zhong, Z., Lin, Z. Q., Bidart, R., Hu, X., Daya, I. B., Li, Z., Zheng, W., Li, J., & Wong, A. K. C. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01308 · Cited by 358 · see citing works

Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks

Schmidhuber, J. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.131 · Cited by 357 · see citing works

DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

Xin, J., Tang, R., Lee, J., Yu, Y., & Lin, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.204 · Cited by 353 · see citing works

Pixel-Adaptive Convolutional Neural Networks

Su, H., Jampani, V., Sun, D., Gallo, O., Learned-Miller, E., & Kautz, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01142 · Cited by 348 · see citing works

Spatially Adaptive Computation Time for Residual Networks

Figurnov, M., Collins, M. D., Zhu, Y., Li, Z., Huang, J., Vetrov, D., & Salakhutdinov, R. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.194 · Cited by 340 · see citing works

Adaptive Computation Time for Recurrent Neural Networks

Graves, A. (2016) · arXiv · doi:10.48550/arxiv.1603.08983 · Cited by 335 · see citing works

Dynamic Multi-Scale Filters for Semantic Segmentation

He, J., Deng, Z., & Qiao, Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00366 · Cited by 320 · see citing works

FastBERT: a Self-distilling BERT with Adaptive Inference Time

Liu, W., Zhou, P., Wang, Z., Zhao, Z., Deng, H., & Ju, Q. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.537 · Cited by 307 · see citing works

More is Less: A More Complicated Network with Less Inference Complexity

Dong, X., Huang, J., Yang, Y., & Yan, S. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.205 · Cited by 305 · see citing works

Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade

Li, X., Liu, Z., Luo, P., Loy, C. C., & Tang, X. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.684 · Cited by 268 · see citing works

ReasoNet

Shen, Y., Huang, P.-S., Gao, J., & Chen, W. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098177 · Cited by 262 · see citing works

Listen to Look: Action Recognition by Previewing Audio

Gao, R., Oh, T.-H., Grauman, K. L., & Torresani, L. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01047 · Cited by 262 · see citing works

Attend, Infer, Repeat: Fast Scene Understanding with Generative ModelsPreprint

Eslami, S. M. A., Heess, N., Weber, T., Tassa, Y., Szepesvári, D., Kavukcuoglu, K., & Hinton, G. E. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.08575 · Cited by 255 · see citing works

Resolution Adaptive Networks for Efficient Inference

Yang, L., Han, Y., Chen, X., Song, S., Dai, J., & Huang, G. M. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00244 · Cited by 250 · see citing works

Real-Time Pedestrian Detection with Deep Network Cascades

Angelova, A., Krizhevsky, A., Vanhoucke, V., Ogale, A. S., & Ferguson, D. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.32 · Cited by 240 · see citing works

Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing

Albericio, J., Judd, P., Hetherington, T., Aamodt, T. M., Jerger, N. E., & Moshovos, A. (2016) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca.2016.11 · Cited by 240 · see citing works

SCSampler: Sampling Salient Clips From Video for Efficient Action Recognition

Korbar, B., Tran, D., & Torresani, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00633 · Cited by 227 · see citing works

AdaFrame: Adaptive Frame Selection for Fast Video Recognition

Wu, Z., Xiong, C., Ma, C., Socher, R., & Davis, L. S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00137 · Cited by 225 · see citing works

Spatio-Temporal Filter Adaptive Network for Video Deblurring

Zhou, S., Zhang, J., Pan, J., Zuo, W., Xie, H., & Ren, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00257 · Cited by 218 · see citing works

Multi-Horizon Time Series Forecasting with Temporal Attention Learning

Fan, C., Zhang, Y., Pan, Y., Li, X., Zhang, C., Yuan, R., Wu, D., Wang, W., Pei, J., & Huang, H. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330662 · Cited by 216 · see citing works

An Efficient Group Recommendation Model With Multiattention-Based Neural Networks

Huang, Z., Xu, X., Zhu, H., & Zhou, M. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2955567 · Cited by 215 · see citing works

SBNet: Sparse Blocks Network for Fast Inference

Ren, M., Pokrovsky, A., Yang, B., & Urtasun, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00908 · Cited by 198 · see citing works

Dynamic ReLU

Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., & Liu, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_21 · Cited by 189 · see citing works

Learning Dynamic Routing for Semantic Segmentation

Li, Y., Song, L., Chen, Y., Li, Z., Zhang, X., Wang, X., & Sun, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00858 · Cited by 186 · see citing works

Attention-Aware Deep Reinforcement Learning for Video Face Recognition

Rao, Y., Lu, J., & Zhou, J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.424 · Cited by 184 · see citing works

Adaptive Convolution for Multi-Relational Learning

Jiang, X., Wang, Q., & Wang, B. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1103 · Cited by 182 · see citing works

Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-Offs by Selective Execution

Liu, L., & Deng, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11630 · Cited by 182 · see citing works

Spatial-Adaptive Network for Single Image Denoising

Chang, M., Li, Q., Feng, H., & Xu, Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_11 · Cited by 181 · see citing works

SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks

Akhlaghi, V., Yazdanbakhsh, A., Samadi, K., Gupta, R. K., & Esmaeilzadeh, H. (2018) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca.2018.00061 · Cited by 180 · see citing works

Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference

Verelst, T., & Tuytelaars, T. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00239 · Cited by 174 · see citing works

Dynamic Region-Aware Convolution

Chen, J., Wang, X., Guo, Z., Zhang, X., & Sun, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00797 · Cited by 172 · see citing works

Learned Image Downscaling for Upscaling Using Content Adaptive Resampler

Sun, W., & Chen, Z. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2970248 · Cited by 169 · see citing works

Online Video Deblurring via Dynamic Temporal Blending Network

Kim, T. H., Lee, K. M., Schölkopf, B., et al. (2017) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.435 · Cited by 165 · see citing works

Learning to Zoom: A Saliency-Based Sampling Layer for Neural Networks

Recasens, A., Kellnhofer, P., Stent, S., Matusik, W., & Torralba, A. B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_4 · Cited by 157 · see citing works

AR-Net: Adaptive Frame Resolution for Efficient Action Recognition

Meng, Y., Lin, C.-C., Panda, R., Sattigeri, P., Karlinsky, L., Oliva, A., Saenko, K., & Feris, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58571-6_6 · Cited by 156 · see citing works

Dynamic Slimmable Network

Li, C., Wang, G., Wang, B., Liang, X., Li, Z., & Chang, X. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00850 · Cited by 154 · see citing works

Adaptive Weighting Multi-Field-Of-View CNN for Semantic Segmentation in Pathology

Tokunaga, H., Teramoto, Y., Yoshizawa, A., & Bise, R. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01288 · Cited by 149 · see citing works

StNet: Local and Global Spatial-Temporal Modeling for Action Recognition

He, D., Zhou, Z., Gan, C., Li, F., Liu, X., Li, Y., Wang, L., & Wen, S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018401 · Cited by 148 · see citing works

Anisotropic Convolutional Networks for 3D Semantic Scene Completion

Li, J., Han, K., Wang, P., Liu, Y., & Yuan, X. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00341 · Cited by 144 · see citing works

Neural Style Transfer via Meta Networks

Shen, F., Yan, S., & Zeng, G. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00841 · Cited by 143 · see citing works

Segmentation-Aware Convolutional Networks Using Local Attention Masks

Harley, A. W., Derpanis, K. G., & Kokkinos, I. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.539 · Cited by 142 · see citing works

Improved Techniques for Training Adaptive Deep Networks

Li, H., Zhang, H., Qi, X., Yang, R., & Huang, G. M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00198 · Cited by 141 · see citing works

Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition

Wu, W., He, D., Tan, X., Chen, S., & Wen, S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00632 · Cited by 140 · see citing works

Dynamic Video Segmentation Network

Xu, Y.-S., Fu, T.-J., Yang, H.-K., & Lee, C. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00686 · Cited by 136 · see citing works

The Right Tool for the Job: Matching Model and Instance Complexities

Schwartz, R., Stanovsky, G., Swayamdipta, S., Dodge, J., & Smith, N. A. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.593 · Cited by 136 · see citing works

AdaBits: Neural Network Quantization With Adaptive Bit-Widths

Jin, Q., Yang, L., & Liao, Z. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00222 · Cited by 135 · see citing works

Scale-Aware Face Detection

Hao, Z., Liu, Y., Qin, H., Yan, J., Li, X., & Hu, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.207 · Cited by 134 · see citing works

Neural Decision Forests for Semantic Image Labelling

Bulò, S. R., & Kontschieder, P. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.18 · Cited by 133 · see citing works

Dynamic Computational Time for Visual Attention

Li, Z., Yang, Y., Liu, X., Zhou, F., Wen, S., & Xu, W. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.145 · Cited by 130 · see citing works

Big/little deep neural network for ultra low power inference

Park, E., Kim, D., Kim, S., Kim, Y.-D., Kim, G., Yoon, S., & Yoo, S. (2015) · 2015 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS) · doi:10.1109/codesisss.2015.7331375 · Cited by 130 · see citing works

HydraNets: Specialized Dynamic Architectures for Efficient Inference

Shazeer, N., Fatahalian, K., Mark, W. R., & Mullapudi, R. T. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00843 · Cited by 124 · see citing works

Funnel Activation for Visual Recognition

Ma, N., Zhang, X., & Sun, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58621-8_21 · Cited by 119 · see citing works

TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning

Wang, X., Yu, F., Wang, R., Darrell, T., & Gonzalez, J. E. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00193 · Cited by 118 · see citing works

Synetgy

Yang, Y., Huang, Q., Wu, B., Zhang, T., Ma, L., Gambardella, G., Blott, M., Lavagno, L., Vissers, K., Wawrzynek, J., et al. (2019) · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · doi:10.1145/3289602.3293902 · Cited by 117 · see citing works

Adaptive Focus for Efficient Video Recognition

Wang, Y., Chen, Z., Jiang, H., Song, S., Han, Y., & Huang, G. M. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01594 · Cited by 112 · see citing works

DynamoNet: Dynamic Action and Motion Network

Diba, A., Sharma, V., Gool, L. V., & Stiefelhagen, R. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00629 · Cited by 111 · see citing works

Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

Marin, D., He, Z., Vajda, P., Chatterjee, P., Tsai, S. S., Yang, F., & Boykov, Y. Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00222 · Cited by 107 · see citing works

Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation

Xie, Z., Zhang, Z., Zhu, X., Huang, G. M., & Lin, S. C.-F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_31 · Cited by 106 · see citing works

WeightNet: Revisiting the Design Space of Weight Networks

Ma, N., Zhang, X., Huang, J., & Sun, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_46 · Cited by 105 · see citing works

Differentiable Patch Selection for Image Recognition

Cordonnier, J.-B., Mahendran, A., Dosovitskiy, A., Weissenborn, D., Uszkoreit, J., & Unterthiner, T. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00238 · Cited by 105 · see citing works

Conditioned Regression Models for Non-blind Single Image Super-Resolution

Riegler, G., Schulter, S., Rüther, M., & Bischof, H. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.67 · Cited by 104 · see citing works

Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image RecognitionPreprint

Wang, Y., Huang, R., Song, S., Huang, Z., & Huang, G. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.15075 · Cited by 101 · see citing works

Incorporating Side Information by Adaptive Convolution

Kang, D., Dhar, D., & Chan, A. B. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01345-8 · Cited by 100 · see citing works

Spanet: Spatial Pyramid Attention Network for Enhanced Image Recognition

Guo, J., Ma, X., Sansom, A., McGuire, M., Kalaani, A., Chen, Q., Tang, S., Yang, Q. X., & Fu, S. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102906 · Cited by 99 · see citing works

You Look Twice: GaterNet for Dynamic Filter Selection in CNNs

Chen, Z., Li, Y., Samy Bengio, A., & Si, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00939 · Cited by 98 · see citing works

Learning to Skim Text

Yu, A. W., Lee, H., & Le, Q. V. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1172 · Cited by 96 · see citing works

FrameExit: Conditional Early Exiting for Efficient Video Recognition

Ghodrati, A., Bejnordi, B. E., & Habibian, A. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01535 · Cited by 92 · see citing works

Dynamic Sampling Networks for Efficient Action Recognition in Videos

Zheng, Y.-D., Liu, Z., Lü, T., & Wang, L. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3007826 · Cited by 86 · see citing works

Watching a Small Portion could be as Good as Watching All: Towards Efficient Video Classification

Fan, H., Xu, Z., Zhu, L., Yan, C. C., Ge, J., & Yang, Y. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/98 · Cited by 85 · see citing works

Question-Guided Hybrid Convolution for Visual Question Answering

Gao, P., Li, H., Li, S., Lu, P., Li, Y., Hoi, S. C. H., & Wang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_29 · Cited by 84 · see citing works

Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference

Wang, Y., Shen, J., Hu, T.-K., Xu, P., Nguyen, T. M., Baraniuk, R. G., Wang, Z., & Lin, Y. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2979669 · Cited by 83 · see citing works

Position-Based Content Attention for Time Series Forecasting with Sequence-to-Sequence RNNs

Cinar, Y. G., Mirisaee, H., Goswami, P., Gaussier, É., Aït-Bachir, A., & Strijov, V. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70139-4_54 · Cited by 80 · see citing works

Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization

Alwassel, H., Heilbron, F. C., & Ghanem, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_16 · Cited by 80 · see citing works

Adaptively Connected Neural Networks

Wang, G., Wang, K., & Lin, L. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00188 · Cited by 75 · see citing works

SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization

Cao, S., Ma, L., Xiao, W., Zhang, C., Liu, Y., Zhang, L., Nie, L., & Yang, Z. Q. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01147 · Cited by 73 · see citing works

IA-RED$^2$: Interpretability-Aware Redundancy Reduction for Vision Transformers

Pan, B., Panda, R., Jiang, Y., Wang, Z., Feris, R., & Oliva, A. (2021) · arXiv · doi:10.48550/arxiv.2106.12620 · Cited by 70 · see citing works

Channel Selection Using Gumbel Softmax

Herrmann, C., Bowen, R. S., & Ramin Zabih, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58583-9_15 · Cited by 68 · see citing works

Text Graph Transformer for Document Classification

Zhang, H., & Zhang, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.668 · Cited by 67 · see citing works

ELASTIC: Improving CNNs With Dynamic Scaling Policies

Wang, H., Kembhavi, A., Farhadi, A., Yuille, A., & Rastegari, M. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00236 · Cited by 65 · see citing works

RubiksNet: Learnable 3D-Shift for Efficient Video Action Recognition

Fan, L., Buch, S., Wang, G., Cao, R., Zhu, Y., Niebles, J. C., & Fei-Fei, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_30 · Cited by 65 · see citing works

End-to-End Learning of Decision Trees and Forests

Hehn, T. M., Kooij, J. F. P., & Hamprecht, F. A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01237-6 · Cited by 64 · see citing works

The cascading neural network: building the Internet of Smart Things

Leroux, S., Bohez, S., De Coninck, E., Verbelen, T., Vankeirsbilck, B., Simoens, P., & Dhoedt, B. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1029-1 · Cited by 59 · see citing works

LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation

Kim, S., Kim, S., Min, D., & Sohn, K. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00029 · Cited by 59 · see citing works

Message Passing Attention Networks for Document Understanding

Nikolentzos, G., Tixier, A. J. ‐., & Vazirgiannis, M. (2020) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v34i05.6376 · Cited by 59 · see citing works

AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

Panda, R., Chen, C.-F. R., Fan, Q., Sun, X., Saenko, K., Oliva, A., & Feris, R. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00748 · Cited by 59 · see citing works

PredictiveNet: An energy-efficient convolutional neural network via zero prediction

Lin, Y., Sakr, C., Kim, Y., & Shanbhag, N. R. (2017) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2017.8050797 · Cited by 55 · see citing works

Pixel-Wise Attentional Gating for Scene Parsing

Kong, S., & Fowlkes, C. C. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00114 · Cited by 54 · see citing works

CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs

Huang, Q., Wang, D., Dong, Z., Gao, Y., Cai, Y., Li, T., Wu, B., Keutzer, K., & Wawrzynek, J. (2021) · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · doi:10.1145/3431920.3439295 · Cited by 53 · see citing works

Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural NetworksPreprint

Davis, A., & Arel, I. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.4461 · Cited by 52 · see citing works

Dynamic Recursive Neural Network

Guo, Q., Yu, Z., Wu, Y., Liang, D., Qin, H., & Yan, J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00529 · Cited by 52 · see citing works

Part-Activated Deep Reinforcement Learning for Action Prediction

Chen, L., Lu, J., Song, Z., & Zhou, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_26 · Cited by 51 · see citing works

EPNet: Learning to Exit with Flexible Multi-Branch Network

Dai, X., Kong, X., & Guo, T. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3411973 · Cited by 48 · see citing works

Dynamic Filtering with Large Sampling Field for ConvNets

Wu, J., Dai, L., Yu, Y., Bajaj, C., & Ji, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_12 · Cited by 47 · see citing works

Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference

Shen, J., Wang, Y., Xu, P., et al. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6025 · Cited by 47 · see citing works

Dynamic Routing Networks

Cai, S., Shu, Y., & Wang, W. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00363 · Cited by 46 · see citing works

Dynamic Network Quantization for Efficient Video Inference

Sun, X., Panda, R., Chen, C.-F. R., Oliva, A., Feris, R., & Saenko, K. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00728 · Cited by 46 · see citing works

Adaptive Convolution for Object Detection

Chen, C., & Ling, Q. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2916104 · Cited by 45 · see citing works

Boosting the Performance of CNN Accelerators with Dynamic Fine-Grained Channel Gating

Hua, W., Zhou, Y. J., De, C., Zhang, Z., & Suh, G. E. (2019) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3352460.3358283 · Cited by 45 · see citing works

InstaNAS: Instance-Aware Neural Architecture Search

Cheng, A., Lin, C. H., Juan, D.-C., Wei, W., & Sun, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5764 · Cited by 45 · see citing works

Any-Precision Deep Neural Networks

Yu, H., Li, H., Shi, H., Huang, T. S., & Hua, G. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17286 · Cited by 43 · see citing works

Improving Document-Level Sentiment Classification Using Importance of Sentences

Choi, G., Oh, S., & Kim, H. (2020) · Entropy · doi:10.3390/e22121336 · Cited by 42 · see citing works

Dynamic attention network for semantic segmentation

Wu, F., Chen, F., Jing, X., Hu, C., Ge, Q., & Ji, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.12.042 · Cited by 41 · see citing works

Inter-Series Attention Model for COVID-19 Forecasting

Jin, X., Wang, Y.-X., & Yan, X. (2021) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611976700.56 · Cited by 41 · see citing works

Fully Dynamic Inference with Deep Neural Networks

Xia, W., Yin, H., Dai, X., & Jha, N. K. (2021) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2021.3056031 · Cited by 39 · see citing works

Dynamic Inference: A New Approach Toward Efficient Video Action Recognition

Wu, W., He, D., Tan, X., Chen, S., Yang, Y., & Wen, S. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00346 · Cited by 38 · see citing works

3D CNNs with Adaptive Temporal Feature Resolutions

Fayyaz, M., Rad, E. B., Diba, A., Noroozi, M., Adeli, E., Gool, L. V., & Gall, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00470 · Cited by 38 · see citing works

Decision Forests, Convolutional Networks and the Models in-BetweenPreprint

Ioannou, Y., Robertson, D., Zikic, D., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.01250 · Cited by 35 · see citing works

Discrete Autoencoders for Sequence ModelsPreprint

Kaiser, Ł., & Bengio, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.09797 · Cited by 33 · see citing works

Learning Instance-wise Sparsity for Accelerating Deep Models

Liu, C., Wang, Y., Han, K., et al. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/416 · Cited by 31 · see citing works

2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video Recognition

Li, H., Wu, Z., Shrivastava, A., & Davis, L. S. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00609 · Cited by 31 · see citing works

Leaving Some Stones Unturned: Dynamic Feature Prioritization for Activity Detection in Streaming Video

Su, Y.-C., & Grauman, K. L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_48 · Cited by 29 · see citing works

Finding decision jumps in text classification

Liu, X., Mou, L., Cui, H., Lu, Z., & Song, S. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.082 · Cited by 28 · see citing works

ILFO: Adversarial Attack on Adaptive Neural Networks

Haque, M., Chauhan, A., Liu, C., & Yang, W. P. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01427 · Cited by 28 · see citing works

S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search

Yuan, Z., Wu, B., Sun, G., Liang, Z., Zhao, S., & Bi, W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58536-5_11 · Cited by 28 · see citing works

Self-Adaptive Network Pruning

Chen, J., Zhu, Z., Li, C., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36708-4_15 · Cited by 26 · see citing works

Energy-efficient Amortized Inference with Cascaded Deep Classifiers

Guan, J., Liu, Y., Liu, Q., & Peng, J. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/302 · Cited by 24 · see citing works

Exponentially Increasing the Capacity-to-Computation Ratio for Conditional Computation in Deep LearningPreprint

Cho, K., & Bengio, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.7362 · Cited by 22 · see citing works

Visual Concept Recognition and Localization via Iterative Introspection

Rosenfeld, A., & Ullman, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54193-8_17 · Cited by 21 · see citing works

Anytime Recognition with Routing Convolutional Networks

Jie, Z., Sun, P., Li, X. S., Feng, J., & Liu, W. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2959322 · Cited by 21 · see citing works

Length Adaptive Recurrent Model for Text Classification

Huang, Z., Ye, Z., Li, S., & Pan, R. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132947 · Cited by 20 · see citing works

Training-Free, Single-Image Super-Resolution Using a Dynamic Convolutional Network

Bhowmik, A., Shit, S., & Seelamantula, C. S. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2752806 · Cited by 19 · see citing works

Skipping RNN State Updates without Retraining the Original Model

Tao, J., Thakker, U., Dasika, G. S., & Beu, J. G. (2019) · doi:10.1145/3362743.3362965 · Cited by 19 · see citing works

BlockCopy: High-Resolution Video Processing with Block-Sparse Feature Propagation and Online Policies

Verelst, T., & Tuytelaars, T. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00511 · Cited by 19 · see citing works

Deep learning modelling techniques: Current progress, applications, advantages, and challenges

Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079 · see citing works

Are Multimodal Transformers Robust to Missing Modality?

Ma, M., Ren, J., Zhao, L., Testuggine, D., & Peng, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01764 · Cited by 198 · see citing works

CondenseNet V2: Sparse Feature Reactivation for Deep Networks

Yang, L., Jiang, H., Cai, R., Wang, Y., Song, S., Huang, G., & Tian, Q. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00357 · Cited by 71 · see citing works

Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches

Wu, Y., Bai, D., Zhang, K., Li, Y., & Yang, F. (2025) · Journal of Materials Informatics · doi:10.20517/jmi.2024.84 · Cited by 20 · see citing works

Dynamic network flow modeling based on cell probe data

Dong, S., Qin, X. D., Zhang, Y., et al. (2010) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5548036

Large-scale dynamic social data representation for structure feature learning

Zhang, X., Zhang, J., & Yang, J. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-189010

Continuous-Time Instantaneous Dynamic User Equilibria on a Real World Traffic Network

Ma, R., & Ban, X. J. (2015) · Transportation Research Board 94th Annual MeetingTransportation Research Board

Modeling and change detection of dynamic network data by a network state space model

Zou, N., & Li, J. (2016) · IISE Transactions · doi:10.1080/0740817x.2016.1198065 · Cited by 28 · see citing works

Enabling Dynamic Analysis of Anonymized Social Network Data

Ding, X. X., & Wang, W. (2012) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc.2012.13