The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.
TVQA: Localized, Compositional Video Question Answering
Open accessAbstract
Recent years have witnessed an increasing interest in image-based question-answering (QA) tasks.However, due to data limitations, there has been much less work on video-based QA.In this paper, we present TVQA, a largescale video QA dataset based on 6 popular TV shows.TVQA consists of 152,545 QA pairs from 21,793 clips, spanning over 460 hours of video.Questions are designed to be compositional in nature, requiring systems to jointly localize relevant moments within a clip, comprehend subtitle-based dialogue, and recognize relevant visual concepts.We provide analyses of this new dataset as well as several baselines and a multi-stream end-to-end trainable neural network framework for the TVQA task.The dataset is publicly available at http://tvqa.cs.unc.edu.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Overall, they obtained promising results on the TVQA [129] and ActivityNetQA [130] datasets.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [129]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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 · cited 2×
“We also use ResNet101 (He et al., 2016) trained on ImageNet (Deng et al., 2009) to extract whole image features.”
MethodFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472 · cited 5×
“Visual Question Answering: Several image-based VQA datasets have recently been constructed, e.g., DAQUAR (Malinowski and Fritz, 2014), VQA (Antol et al., 2015), COCO-Q (Ren et al., 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al., 2015), Visual7W (Zhu et al., 2016), CLEVR (Johnson et al., 2017), etc. Addit…”
BackgroundMethodInfluentialFaster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · cited 5×
“ImageNet Features: We extract the pooled 2048D feature of the last block of ResNet101.”
BackgroundMethodInfluentialGlove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · cited 4×
“A subtitle S, which contains a set of sentences, is flattened into a long sequence of words and GloVe (Pennington et al., 2014) is used to embed the words.”
BackgroundMethodInfluentialSQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · cited 2×
“With the same goal in mind, Rajpurkar et al. (2016) introduced the SQuAD dataset, but their answers are specific spans from long passages.”
BackgroundVQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., & Gool, L. V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_2 · Cited by 4,232
Teaching Machines to Read and ComprehendPreprint
Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,855 · cited 1×
“Text Question Answering: The related task of text-based question answering has been extensively explored (Richardson et al., 2013; Weston et al., 2015; Rajpurkar et al., 2016; Hermann et al., 2015; Hill et al., 2015).”
BackgroundCLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., & Girshick, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.215 · Cited by 1,781
Bidirectional Attention Flow for Machine ComprehensionPreprint
Seo, M. J., Kembhavi, A., Farhadi, A., & Hajishirzi, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01603 · Cited by 1,698 · cited 2×
“The context matching module is adopted from the context-query attention layer from previous works (Seo et al., 2017; Yu et al., 2018a).”
MethodKiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R. S., Torralba, A., Urtasun, R., & Fidler, S. (2015) · arXiv · doi:10.48550/arxiv.1506.06726 · Cited by 1,293 · cited 3×
“We embed sentences into vectors using TFIDF, SkipThought (Kiros et al., 2015), or averaged GloVe (Pennington et al.”
MethodDense-Captioning Events in Videos
Krishna, R., Hata, K., Ren, F., Fei-Fei, L., & Niebles, J. C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.83 · Cited by 1,279
(2016) · doi:10.1007/978-3-319-46475-6_5 · Cited by 1,081 · cited 2×
“, 2016) and referring expression comprehension (Hu et al., 2016; Yu et al., 2016; Nagaraja et al., 2016; Yu et al., 2017, 2018b).”
BackgroundSelf-citationDeep Visual-Semantic Alignments for Generating Image Descriptions
Karpathy, A., & Fei-Fei, L. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2598339 · Cited by 1,048
Localizing Moments in Video with Natural Language
Hendricks, L. A., Wang, O., Shechtman, E., Šivic, J., Darrell, T., & Russell, B. C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.618 · Cited by 1,034 · cited 1×
“Most recently, moment localization was proposed in (Hendricks et al., 2017; Gao et al., 2017), where the goal is to localize a short moment from a long video sequence given a query description.”
BackgroundTALL: Temporal Activity Localization via Language Query
Gao, J., Sun, C., Yang, Z., & Nevatia, R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.563 · Cited by 936 · cited 1×
“Most recently, moment localization was proposed in (Hendricks et al., 2017; Gao et al., 2017), where the goal is to localize a short moment from a long video sequence given a query description.”
BackgroundQANet: Combining Local Convolution with Global Self-Attention for Reading ComprehensionPreprint
Yu, A. W., Dohan, D., Luong, M.-T., Zhao, R., Chen, K., Norouzi, M., & Le, Q. V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.09541 · Cited by 882 · cited 3×
“The context matching module is adopted from the context-query attention layer from previous works (Seo et al., 2017; Yu et al., 2018a).”
BackgroundMethodMAttNet: Modular Attention Network for Referring Expression Comprehension
Yu, L., Lin, Z., Shen, X., Yang, J., Lu, X., Bansal, M., & Berg, T. L. (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.2018.00142 · Cited by 876
Self-citationLearning Deep Structure-Preserving Image-Text Embeddings
Wang, L., Li, Y., & Lazebnik, S. (2016) · 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.2016.541 · Cited by 795 · cited 1×
“For image-based object grounding, there has been much work on phrase grounding (Plummer et al., 2015; Wang et al., 2016b; Rohrbach et al., 2016) and referring expression comprehension (Hu et al., 2016; Yu et al., 2016; Nagaraja et al., 2016; Yu et al., 2017, 2018b).”
BackgroundVisual7W: Grounded Question Answering in Images
Zhu, Y., Groth, O., Bernstein, M., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.540 · Cited by 794 · cited 5×
“Visual Question Answering: Several image-based VQA datasets have recently been constructed, e.g., DAQUAR (Malinowski and Fritz, 2014), VQA (Antol et al., 2015), COCO-Q (Ren et al., 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al., 2015), Visual7W (Zhu et al., 2016), CLEVR (Johnson et al., 2017), etc. Addit…”
BackgroundInfluentialMCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text
Richardson, M., Burges, C., & Renshaw, E. (2013) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1020 · Cited by 778 · cited 2×
“Text Question Answering: The related task of text-based question answering has been extensively explored (Richardson et al., 2013; Weston et al., 2015; Rajpurkar et al., 2016; Hermann et al., 2015; Hill et al., 2015).”
BackgroundMethodTowards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Weston, J., Bordes, A., Chopra, S., Rush, A. M., van Merriënboer, B., Joulin, A., & Mikolov, T. (2015) · arXiv · doi:10.48550/arxiv.1502.05698 · Cited by 670 · cited 2×
“Weston et al. (2015) designed a set of tasks with automatically generated QAs to evaluate the textual reasoning ability of artificial agents and Hermann et al. (2015); Hill et al. (2015) constructed the cloze dataset on top of an existing corpus.”
BackgroundMovieQA: Understanding Stories in Movies through Question-Answering
Tapaswi, M., Zhu, Y., Stiefelhagen, R., Torralba, A. B., Urtasun, R., & Fidler, S. (2016) · 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.2016.501 · Cited by 646 · cited 5×
“MovieQA (Tapaswi et al., 2016) is most similar to our dataset, with both multiple choice questions and timestamp annotation.”
BackgroundMethodInfluentialExploring Models and Data for Image Question AnsweringPreprint
Ren, M., Kiros, R., & Zemel, R. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.02074 · Cited by 630 · cited 3×
“Visual Question Answering: Several image-based VQA datasets have recently been constructed, e.g., DAQUAR (Malinowski and Fritz, 2014), VQA (Antol et al., 2015), COCO-Q (Ren et al., 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al., 2015), Visual7W (Zhu et al., 2016), CLEVR (Johnson et al., 2017), etc. Addit…”
BackgroundMethodVQA: Visual Question Answering: www.visualqa.org
Agrawal, A., Lu, J., Antol, S., Mitchell, M., Zitnick, C. L., Parikh, D., & Batra, D. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0966-6 · Cited by 570 · cited 5×
“Alternative methods include sampling negative answers from other questions’ correct answers, either based on semantic similarity (Das et al., 2017; Jang et al., 2017) or randomly (Antol et al., 2015; Das et al., 2017).”
BackgroundMethodInfluentialNatural Language Object Retrieval
Hu, R., Xu, H., Rohrbach, M., Feng, J., Saenko, K., & Darrell, T. (2016) · 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.2016.493 · Cited by 538 · cited 1×
“For image-based object grounding, there has been much work on phrase grounding (Plummer et al., 2015; Wang et al., 2016b; Rohrbach et al., 2016) and referring expression comprehension (Hu et al., 2016; Yu et al., 2016; Nagaraja et al., 2016; Yu et al., 2017, 2018b).”
BackgroundTGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering
Jang, Y., Song, Y., Yu, Y., Kim, Y., & Kim, 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.2017.149 · Cited by 527 · cited 6×
“Visual Question Answering: Several image-based VQA datasets have recently been constructed, e.g., DAQUAR (Malinowski and Fritz, 2014), VQA (Antol et al., 2015), COCO-Q (Ren et al., 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al., 2015), Visual7W (Zhu et al., 2016), CLEVR (Johnson et al., 2017), etc. Addit…”
BackgroundMethodInfluentialThe Goldilocks Principle: Reading Children's Books with Explicit Memory RepresentationsPreprint
Hill, F., Bordes, A., Chopra, S., & Weston, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.02301 · Cited by 478 · cited 2×
“Weston et al. (2015) designed a set of tasks with automatically generated QAs to evaluate the textual reasoning ability of artificial agents and Hermann et al. (2015); Hill et al. (2015) constructed the cloze dataset on top of an existing corpus.”
BackgroundModeling Context Between Objects for Referring Expression Understanding
Nagaraja, V., Morariu, V. I., & Davis, L. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_48 · Cited by 469 · cited 2×
“, 2016) and referring expression comprehension (Hu et al., 2016; Yu et al., 2016; Nagaraja et al., 2016; Yu et al., 2017, 2018b).”
A dataset for Movie Description
Rohrbach, A., Rohrbach, M., Tandon, N., & Schiele, B. (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.2015.7298940 · Cited by 457 · cited 3×
“, 2017), LSMDC (Rohrbach et al., 2015), TRECVID (Over et al.”
MethodAre You Talking to a Machine? Dataset and Methods for Multilingual Image Question AnsweringPreprint
Gao, H., Mao, J., Zhou, J., Huang, Z., Wang, L., & Xu, W. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05612 · Cited by 441 · cited 4×
“, 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al.”
BackgroundInfluentialDas, A., Kottur, S., Gupta, K., Singh, A., Jain, D., Moura, J. M. F., Parikh, D., & Batra, D. (2017) · Cited by 432
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain InputPreprint
Malinowski, M., & Fritz, M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.0210 · Cited by 432 · cited 3×
“Visual Question Answering: Several image-based VQA datasets have recently been constructed, e.g., DAQUAR (Malinowski and Fritz, 2014), VQA (Antol et al., 2015), COCO-Q (Ren et al., 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al., 2015), Visual7W (Zhu et al., 2016), CLEVR (Johnson et al., 2017), etc. Addit…”
BackgroundMethodGrounding of Textual Phrases in Images by Reconstruction
Rohrbach, A., Rohrbach, M., Hu, R., Darrell, T., & Schiele, B. (2016) · Max Planck Digital Library · Cited by 422
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Weston, J., Bordes, A., Chopra, S., Rush, A. M., van Merriënboer, B., Joulin, A., & Mikolov, T. (2016) · International Conference on Learning Representations · Cited by 261
Bottom-Up and Top-Down Attention for Image Captioning and VQA.Preprint
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S. J., & Zhang, L. (2017) · arXiv (Cornell University) · Cited by 252
A Joint Speaker-Listener-Reinforcer Model for Referring Expressions
Yu, L., Tan, H. X., Bansal, M., & Berg, T. L. (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.2017.375 · Cited by 250 · cited 1×
“For image-based object grounding, there has been much work on phrase grounding (Plummer et al., 2015; Wang et al., 2016b; Rohrbach et al., 2016) and referring expression comprehension (Hu et al., 2016; Yu et al., 2016; Nagaraja et al., 2016; Yu et al., 2017, 2018b).”
BackgroundSelf-citationUncovering the Temporal Context for Video Question Answering
Zhu, L., Xu, Z., Yang, Y., & Hauptmann, A. G. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1033-7 · Cited by 217 · cited 4×
“Visual Question Answering: Several image-based VQA datasets have recently been constructed, e.g., DAQUAR (Malinowski and Fritz, 2014), VQA (Antol et al., 2015), COCO-Q (Ren et al., 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al., 2015), Visual7W (Zhu et al., 2016), CLEVR (Johnson et al., 2017), etc. Addit…”
InfluentialDeepStory: Video Story QA by Deep Embedded Memory Networks
Kim, K.-M., Heo, M.-O., Choi, S.-H., & Zhang, B. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/280 · Cited by 167 · cited 5×
“Visual Question Answering: Several image-based VQA datasets have recently been constructed, e.g., DAQUAR (Malinowski and Fritz, 2014), VQA (Antol et al., 2015), COCO-Q (Ren et al., 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al., 2015), Visual7W (Zhu et al., 2016), CLEVR (Johnson et al., 2017), etc. Addit…”
BackgroundMethodInfluentialNPCEditor: Creating Virtual Human Dialogue Using Information Retrieval Techniques
Leuski, A., & Traum, D. R. (2011) · AI Magazine · doi:10.1609/aimag.v32i2.2347 · Cited by 102 · cited 1×
“Similar approaches have also been used in dialogue systems (Jafarpour et al., 2010; Leuski and Traum, 2011), picking the appropriate responses to an utterance from a predefined human conversational corpus.”
MethodMarioQA: Answering Questions by Watching Gameplay Videos
Mun, J., Seo, P. H., Jung, I., & Han, B. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.312 · Cited by 95 · cited 3×
“Visual Question Answering: Several image-based VQA datasets have recently been constructed, e.g., DAQUAR (Malinowski and Fritz, 2014), VQA (Antol et al., 2015), COCO-Q (Ren et al., 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al., 2015), Visual7W (Zhu et al., 2016), CLEVR (Johnson et al., 2017), etc. Addit…”
MethodMaharaj, T., Ballas, N., Rohrbach, A., Courville, A. C., & Pal, C. (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.2017.778 · Cited by 93 · cited 5×
“Visual Question Answering: Several image-based VQA datasets have recently been constructed, e.g., DAQUAR (Malinowski and Fritz, 2014), VQA (Antol et al., 2015), COCO-Q (Ren et al., 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al., 2015), Visual7W (Zhu et al., 2016), CLEVR (Johnson et al., 2017), etc. Addit…”
MethodInfluentialVisual Madlibs: Fill in the blank Image Generation and Question AnsweringPreprint
Yu, L., Park, E., Berg, A. C., & Berg, T. L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00278 · Cited by 75 · cited 5×
“Visual Question Answering: Several image-based VQA datasets have recently been constructed, e.g., DAQUAR (Malinowski and Fritz, 2014), VQA (Antol et al., 2015), COCO-Q (Ren et al., 2015a), FM-IQA (Gao et al., 2015), Visual Madlibs (Yu et al., 2015), Visual7W (Zhu et al., 2016), CLEVR (Johnson et al., 2017), etc. Addit…”
BackgroundInfluentialSelf-citationObject Referring in Videos with Language and Human Gaze
Vasudevan, A. B., Dai, D., & Gool, L. V. (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.2018.00434 · Cited by 67
Obj2Text: Generating Visually Descriptive Language from Object Layouts
Yin, X., & Ordóñez, V. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1017 · Cited by 60 · cited 1×
“Visual Concept Features: Recent work (Yin and Ordonez, 2017) found that using detected object”
BackgroundFilter, Rank, and Transfer the Knowledge: Learning to Chat
Jafarpour, S. (2010) · Cited by 51
Flagged first, then by citations
2 of the 49 references couldn’t be matched to a known paper.
Research works printed but not linked: 8
- Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Mar- garet Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. 2015. Vqa: Visual question an- swering. ICCV, pages 2425-2433.
- Ronghang Hu, Huazhe Xu, Marcus Rohrbach, Jiashi Feng, Kate Saenko, and Trevor Darrell. 2016. Natu- ral language object retrieval. CVPR.
- Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron Courville, and Christopher Pal. 2017b. A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering. CVPR.
- Paul Over, Jon Fiscus, Greg Sanders, David Joy, Mar- tial Michel, George Awad, Alan Smeaton, Wessel Kraaij, and Georges Quénot. 2014. Trecvid 2014-an overview of the goals, tasks, data, evaluation mech- anisms and metrics. In Proceedings of TRECVID, page 52.
- Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. 2015. Flickr30k entities: Collecting region-to-phrase correspondences for richer image- to-sentence models. In ICCV.
- Licheng Yu, Zhe Lin, Xiaohui Shen, Jimei Yang, Xin Lu, Mohit Bansal, and Tamara L Berg. 2018b. Mat- tnet: Modular attention network for referring expres- sion comprehension. In CVPR.
- Licheng Yu, Patrick Poirson, Shan Yang, Alexander C Berg, and Tamara L Berg. 2016. Modeling context in referring expressions. In ECCV.
- Licheng Yu, Hao Tan, Mohit Bansal, and Tamara L Berg. 2017. A joint speakerlistener-reinforcer model for referring expressions. In CVPR.
Cited by
Sorted by citations
From Recognition to Cognition: Visual Commonsense Reasoning
Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00688 · Cited by 769
“While past work has asked humans to write several counterfactual choices for each correct answer [75, 46], this process is expensive.”
BackgroundLess is More: CLIPBERT for Video-and-Language Learning via Sparse Sampling
Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T. L., Bansal, M., & Liu, 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/cvpr46437.2021.00725 · Cited by 639
“Popular video-andlanguage tasks include text-to-video retrieval [75, 28, 54], video captioning [75, 82, 28, 54, 40], video question answering [74, 23, 33], and moment retrieval [2, 18, 35].”
BackgroundInfluentialSelf-citationLearning 2D Temporal Adjacent Networks for Moment Localization with Natural Language
Zhang, S., Peng, H., Fu, J., & Luo, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6984 · Cited by 513
Zhao, F., Zhang, C., & Geng, B. (2024) · ACM Computing Surveys · doi:10.1145/3649447 · Cited by 480
A Corpus for Reasoning about Natural Language Grounded in Photographs
Suhr, A., Zhou, S., Zhang, A. Q., Zhang, I., Bai, H., & Artzi, Y. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1644 · Cited by 628
“, 2018) Visual Question Answering TVQA (Lei et al., 2018) Video Question Answering 4 4”
Background
Metadata
- Authors
- Lei, J., Yu, L., Bansal, M., & Berg, T.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 1369-1379
- OpenAlex
- W2963541336
- Cited by
- 541
- References
- 49
- Citations by year
- 2026: 39 · 2025: 50 · 2024: 65 · 2023: 93 · 2022: 90 · 2021: 101 · 2020: 70 · 2019: 32 · 2018: 1
- FWCI
- 15.13
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1405822, 1562098, 1633295)
- Army Research Office (W911NF)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- video question answering, video localization, television series, end-to-end learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
