Segmentation-Aware Convolutional Networks Using Local Attention Masks
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We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision. To obtain segmentation information, we set up a CNN to provide an embedding space where region co-membership can be estimated based on Euclidean distance. We use these embeddings to compute a local attention mask relative to every neuron position. We incorporate such masks in CNNs and replace the convolution operation with a “segmentation-aware” variant that allows a neuron to selectively attend to inputs coming from its own region. We call the resulting network a segmentation-aware CNN because it adapts its filters at each image point according to local segmentation cues, while at the same time remaining fully-convolutional. We demonstrate the merit of our method on two widely different dense prediction tasks, that involve classification (semantic segmentation) and regression (optical flow). Our results show that in semantic segmentation we can replace DenseCRF inference with a cascade of segmentation-aware filters, and in optical flow we obtain clearly sharper responses than the ones obtained with comparable networks that do not use segmentation. In both cases segmentation-aware convolution yields systematic improvements over strong baselines.
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Geometry-Aware Distillation for Indoor Semantic Segmentation
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Pattern-Structure Diffusion for Multi-Task Learning
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Know What Your Neighbors Do : 3D Semantic Segmentation of Point Clouds
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A Dynamic Multi-Scale Voxel Flow Network for Video Prediction
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Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds
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Dense Convolutional Networks for Semantic Segmentation
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LogicSeg: Parsing Visual Semantics with Neural Logic Learning and Reasoning
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Panoptic-Based Image Synthesis
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Dynamic Filtering with Large Sampling Field for ConvNets
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Semantic Hierarchy-Aware Segmentation
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Stochastic Segmentation with Conditional Categorical Diffusion Models
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Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans
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Does Generative Face Completion Help Face Recognition?
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Rethinking Attentive Object Detection via Neural Attention Learning
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Multi-scale YOLACT for instance segmentation
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AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Learning Model For Scene Understanding
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Efficient Visual Recognition: A Survey on Recent Advances and Brain-inspired Methodologies
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Image Inpainting for Digital Dunhuang Murals Using Partial Convolutions and Sliding Window Method
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Person Retrieval in Video Surveillance Using Deep Learning–Based Instance Segmentation
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Structure-Informed Shadow Removal Networks
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Fine-grained Visible Watermark Removal
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Visual Micro-Pattern Propagation
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Multi-Scale Aggregation Network for Direct Face Alignment
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Error Correction for Dense Semantic Image Labeling
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Image inpainting with contextual attention and partial convolution
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Semi-convolutional Operators for Instance SegmentationPreprint
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- Authors
- Harley, A. W., Derpanis, K. G., & Kokkinos, I.
- Affiliations
- Carnegie Mellon University (US)
- Toronto Metropolitan University (CA)
- Meta (United States) (US)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5048-5057
- OpenAlex
- W2747768235
- Cited by
- 142
- References
- 67
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 8 · 2023: 22 · 2022: 14 · 2021: 26 · 2020: 20 · 2019: 28 · 2018: 16
- FWCI
- 5.04
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- adaptive filtering, semantic segmentation, optical flow, dense prediction, spatial precision, fully convolutional network
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