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REVIEW 3 major objections 6 minor 40 references

VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

T0 review · 3 major / 6 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This paper introduces VideoNavQA, a benchmark that replaces full navigation in embodied question answering with near-optimal trajectory videos, and reports that a vision-language model beats the best language-only baseline by about eight…

desk verdict VideoNavQA is a genuinely useful benchmark that decouples navigation from visual reasoning in embodied QA, with a wider question space than prior datasets; the main results are plausible, but the evaluation protocol around frame subsampling is underspecified. read the letter →

arxiv 1908.04950 v1 pith:IIKMJXTE submitted 2019-08-14 cs.CV cs.AIcs.CLcs.LG

Cătălina Cangea , Eugene Belilovsky , Pietro Liò , Aaron Courville This is my paper · ORCID
classification cs.CVcs.AIcs.CLcs.LG
keywords VideoNavQAembodiedquestionansweringvisualnavigationHouse3Dbenchmarkdatasetfunctionaltemplates
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper attacks the question of whether embodied question answering (EQA) is feasible with current vision-and-language methods. It builds VideoNavQA, a dataset of about 101,000 question-video pairs rendered from House3D houses; the videos follow near-optimal shortest paths so the agent is not penalized for navigation failures. The paper asks whether standard VQA-style models can use these visual streams to answer a much richer question set than earlier EQA benchmarks, with 28 templates in 8 categories and 70 possible answers. On the test set the best model, Concat-CNN2D, reaches 64.47% accuracy, while the strongest language-only LSTM reaches 56.49% and bag-of-words 49.02%. The authors take this gap as evidence that the visual context carries signal beyond dataset bias, making VideoNavQA a feasible testbed for isolating visual reasoning from navigation.

What carries the argument

The load-bearing mechanism is the dataset-generation pipeline: in the House3D grid representation the paper computes shortest paths between rooms, renders those paths as videos, parses every frame with semantic segmentation and depth to index visible objects and their rooms, and instantiates 28 functional question templates whose associated programs are executed on that ground truth to produce answers. On the modeling side, the leading mechanism is the simple concatenation architecture, Concat-CNN2D: a per-frame 2D CNN plus LSTM summarizes the video, the question LSTM summarizes the text, and the two vectors are merged by an MLP; the paper also adapts FiLM and MAC to the temporal setting. The pipeline is doing the work of ensuring labels are computable and that language-only models cannot trivially solve every instance.

What would settle it

For each test question, use the template program and semantic ground truth to check whether the subsampled 35 frames contain all objects and rooms needed to compute the answer; if a nontrivial fraction of questions lack their evidence frames, rerun Concat-CNN2D and the LSTM on subsamples that guarantee evidence is present and see whether the reported eight-point gap changes.

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Extended reading notes

Core claim

The central discovery is a decoupling: by replacing full EQA navigation with near-ideal trajectories, the paper converts the embodied task into a video QA task while increasing question difficulty. Ground-truth answers are generated by parsing semantic renderings of every frame and executing template programs such as filter, count, and get_attr. Evaluated models include two language-only baselines, two video-only CNNs, concatenated visual+language models, per-frame FiLM with attention or global max-pooling, a temporal multi-hop FiLM, and a MAC-LSTM adaptation. Concat-CNN2D, which concatenates an LSTM question embedding with per-frame CNN features aggregated by an LSTM, attains 64.47% test accuracy; all visual+language models lie between about 62.3% and 64.5%, clearly above the LSTM's 56.49%. The paper interprets this as initial validation that the dataset construction lets visual reasoning generalize across new houses.

Load-bearing premise

The random subsampling in Section 5.1 keeps only one frame from each block of four, while answers are labeled from the full video, so if the frames containing a question's target objects are dropped the model cannot see the evidence its label requires; this could depress all models and inflate the measured visual improvement.

Editorial extensions

If this is right

  • If the reported gap is real, VQA-style methods can take advantage of visual context in EQA once navigation is controlled, suggesting that navigation, not perception alone, was a major bottleneck in earlier EQA failures.
  • VideoNavQA provides a diagnostic scale: category-level results show existence and attribute questions are easier while object-type identification and counting remain hard, giving concrete targets for future video QA models.
  • The small spread among all multimodal models, roughly two accuracy points, suggests current video QA architectures saturate on this benchmark and that gains should come from better temporal selection rather than simple fusion.
  • The 70-answer, 28-template design permits standard Yes/No, other, and number reporting, which makes it possible to compare new video QA methods without a navigation component.
  • By providing near-optimal trajectories, the dataset establishes a lower bound on achievable EQA performance that navigation-aware agents should eventually exceed.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: a principled extension is to annotate each question with a set of evidence frames derived from the ground-truth program, then measure how often the random 35-frame subsample retains them; this would directly test the weakest assumption about dropped visual evidence.
  • Editorial inference: the same template-and-program pipeline could be reused with other rendered or photorealistic indoor environments, turning VideoNavQA into a portable video QA benchmark that is independent of any particular simulator.
  • Editorial inference: a counterfactual test in which the question is paired with a video from a different house would reveal how much of the visual gain comes from genuine grounding rather than from question-to-answer priors.
  • Editorial inference: because all multimodal models cluster near 64%, a more sensitive benchmark could increase the fraction of counting and comparison questions, where current models hover around 50%.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. VideoNavQA is a new dataset for visual question answering in an embodied setting with near-optimal navigation trajectories. The dataset contains approximately 101,000 video-question pairs generated in the House3D environment, with 28 question templates across 8 categories. The authors evaluate several existing VQA-style models adapted to video input, plus language-only and vision-only baselines. The main empirical claim is that multimodal models outperform language-only baselines on the test set, with Concat-CNN2D achieving 64.47% accuracy versus the LSTM's 56.49%.

Significance. If the central claim holds, VideoNavQA is a useful diagnostic benchmark for decoupling visual reasoning from navigation in embodied question answering. The test environments are disjoint from training, and the dataset and code are released. The comparison of a diverse set of baselines provides an initial mapping of task difficulty. However, the quantitative conclusion is not fully supported because the evaluation protocol for frame subsampling is ambiguous (see major comments), and no variance estimates are reported. With clarification and additional analysis, the dataset could serve as a solid testbed for VQA-style methods in embodied settings.

major comments (3)
  1. [5.1 (Setup); 3.1 (Ground truth)] The randomized 4-to-1 frame subsampling is described only as a training-time operation ('On each training iteration'), but the test-time protocol is never specified. If the same subsampling is applied at test time, the ground-truth answers computed from the full video (Section 3.1) will be unanswerable for any example whose answer-bearing frames are dropped; if it is not applied, the models are trained on videos of at most 35 frames and evaluated on videos of up to 140 frames, a distribution shift that is not discussed. In both cases the reported gap between Concat-CNN2D (64.47%) and LSTM (56.49%) in Table 3 cannot be cleanly attributed to visual reasoning. Please state the exact evaluation preprocessing and report an oracle experiment measuring how many test questions remain visually answerable under the chosen protocol.
  2. [Table 3 / Section 5.2] All accuracy numbers are reported as point estimates with no error bars, confidence intervals, or multiple-seed runs. This is particularly problematic for the claim that Concat-CNN2D 'outperforms the other models': its margin over FiLM-AT is 64.47 versus 64.08, and over Concat-CNN3D is 64.47 versus 64.00, differences that may not be robust to training noise. The main visual-versus-language claim would also be more convincing with variance estimates.
  3. [3.1 (Visual information)] The ground-truth extraction pipeline is described in a single sentence: objects are identified via SUNCG semantic rendering and then linked 'via depth rendering to the current room the agent is in, or to an adjacent one.' This does not specify how objects visible through doorways or at room boundaries are assigned to rooms, nor how the semantic rendering is aligned with the rendered video frames. Since the correctness of the entire dataset depends on this mapping, please provide the full algorithm (or pseudo-code) and include a validation study, such as human agreement on a random sample or comparison with an independent scene-graph source.
minor comments (6)
  1. [5.1] The sentence 'we extract features from an object detector pre-trained on a set of 2000 frames that are not part of the dataset, which we initialize with the output from the 10th layer of a Faster R-CNN' is ambiguous about what is initialized with what; please rephrase.
  2. [Appendix] The hyperparameter list says 'we have ran several combinations'; change to 'we ran' or 'we have run.'
  3. [Figure 2] The rightmost panel's title appears clipped; consider adding a note with the exact maximum video length and the distribution in the caption.
  4. [Appendix] The question-template notation such as 'set(<art> <attr{}> <obj_type{}>)' is not defined; please explain how these templates are instantiated to natural-language questions.
  5. [Appendix] The MAC hyperparameters cite reference [5] for the 'ramp-up/down Adam learning schedule,' but [5] is the MUREL paper; please verify this citation.
  6. [5.2] The claim that 'no significant difference in validation accuracy is noticed when training the Concat-CNN3D model with and without pre-trained features' should be supported with the actual validation accuracies.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: VideoNavQA is an empirical benchmark paper whose central claim rests on held-out test accuracy; the few author-overlapping citations are background and not load-bearing.

full rationale

The paper's central contribution is the construction of a new dataset and an empirical comparison of VQA-style models on it. There is no first-principles derivation whose output is fed back as an input. The claimed result in Section 5.2 (Concat-CNN2D at 64.47% vs. LSTM at 56.49%) is a measured test-set accuracy: models are trained on question-video pairs, hyperparameters are selected by monitoring validation accuracy, and the final numbers are reported on a disjoint test split (Table 1), so the result is not fitted to the test answers. The only author-overlapping citations are [1] (Anand et al., used to motivate language-only baselines), [27] and [33] (FiLM and multi-hop modulation, used as architectural components), and [4] (a generalization study, cited in related work). These are not load-bearing for the empirical claim: no uniqueness theorem or prior derivation is invoked to forbid alternatives or to force the reported gap. The randomized frame subsampling described in Section 5.1 ('On each training iteration, every section of 4 frames is reduced to 1') raises a possible evaluation-protocol ambiguity about whether the same subsampling is applied at test time, and could affect the interpretation of the visual-versus-language gap; but that is a construct-validity and robustness concern, not a circularity in which an output is equivalent to an input by construction. Accordingly, no circular step is identified; the score reflects only the presence of minor, non-load-bearing self-citations.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No analytical derivation or fitted constants appear in this paper; the central result is an empirical benchmark comparison. The listed assumptions are the data-generation premises that the accuracy numbers depend on.

assumptions (3)
  • domain assumption SUNCG semantic rendering provides correct and complete ground truth for visible objects in each video frame.
    Answer labels are computed from these semantic parses; any missed or mislabeled object propagates into ground truth answers (Section 3.1).
  • domain assumption Randomly subsampling every block of 4 frames to 1 frame preserves sufficient evidence to answer the question.
    The ground truth is derived from the full video while models see only 35 sampled frames; objects visible only in skipped frames cannot be seen by the model (Section 5.1).
  • domain assumption Shortest-path trajectories between rooms are nearly ideal and include the visual information needed to answer a sampled question.
    Dataset construction assumes these paths are informative enough for QA; no verification that all sampled questions are answerable from the rendered frames alone (Section 3.1).

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Cite this review

Pith. "Pith review of VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering." pith.science (2026). https://pith.science/paper/IIKMJXTE

@misc{pith2026190804950,
  author       = {Pith},
  title        = {Pith review of: VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IIKMJXTE}},
  note         = {Machine review of arXiv:1908.04950}
}
read the original abstract

Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely on its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language understanding in order to perform complex reasoning in the visual world. However, initial advancements combining standard vision and language methods with imitation and reinforcement learning algorithms have shown EQA might be too complex and challenging for these techniques. In order to investigate the feasibility of EQA-type tasks, we build the VideoNavQA dataset that contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task. We investigate several models, adapted from popular VQA methods, on this new benchmark. This establishes an initial understanding of how well VQA-style methods can perform within this novel EQA paradigm.

Figures

Figures reproduced from arXiv: 1908.04950 by the authors.

Figure 1
Figure 1. High-level description of the VideoNavQA task and our approach: the VQA sys [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. VideoNavQA dataset distribution. (Left:) Proportions for each question category. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. (Left:) Concat-CNN3D processes the entire video. (Right:) Concat-CNN2D ag [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: (Left:) Per-frame FiLM model. Video frames are processed separately by the [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Comparative performance of the models on each question category. Questions [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

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Works this paper leans on

40 extracted references · 24 canonical work pages

  1. [1]

    Blindfold Baselines for Embodied QA

    Ankesh Anand, Eugene Belilovsky, Kyle Kastner, Hugo Larochelle, and Aaron Courville. Blindfold Baselines for Embodied QA. arXiv preprint arXiv:1811.05013, 2018

    arXiv 2018

  2. [2]

    VQA: Visual question answering

    Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. VQA: Visual question answering. In Pro- ceedings of the IEEE international conference on computer vision , pages 2425–2433, 2015

    work page 2015

  3. [3]

    Neural machine translation by jointly learning to align and translate

    Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014

    arXiv 2014

  4. [4]

    Systematic Generalization: What Is Required and Can It Be Learned? arXiv preprint arXiv:1811.12889, 2018

    Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, and Aaron Courville. Systematic Generalization: What Is Required and Can It Be Learned? arXiv preprint arXiv:1811.12889, 2018

    arXiv 2018

  5. [5]

    MUREL: Multimodal Relational Reasoning for Visual Question Answering

    Remi Cadene, Hedi Ben-Younes, Nicolas Thome, and Matthieu Cord. MUREL: Multimodal Relational Reasoning for Visual Question Answering. In IEEE Con- ference on Computer Vision and Pattern Recognition CVPR , 2019. URL http: //remicadene.com/pdfs/paper_cvpr2019.pdf

    work page 2019

  6. [6]

    Learning phrase representations using RNN encoder-decoder for statistical machine translation

    Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014. CANGEA, BELILOVSKY , LIÒ, COURVILLE: VIDEONA VQA 11

    arXiv 2014

  7. [7]

    Embodied Question Answering

    Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra. Embodied Question Answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

    work page 2018

  8. [8]

    Neural Modular Control for Embodied Question Answering

    Abhishek Das, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra. Neural Modular Control for Embodied Question Answering. arXiv preprint arXiv:1810.11181, 2018

    arXiv 2018

Show all 40 references
  1. [9]

    Multimodal compact bilinear pooling for visual question answering and visual grounding

    Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, and Mar- cus Rohrbach. Multimodal compact bilinear pooling for visual question answering and visual grounding. arXiv preprint arXiv:1606.01847, 2016

    2016 arXiv

  2. [10]

    Deep sparse rectifier neural net- works

    Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural net- works. In Proceedings of the fourteenth international conference on artificial intelli- gence and statistics, pages 315–323, 2011

    2011

  3. [11]

    IQA: Visual question answering in interactive environments

    Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, and Ali Farhadi. IQA: Visual question answering in interactive environments. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4089–4098, 2018

    2018

  4. [12]

    Long short-term memory

    Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computa- tion, 9(8):1735–1780, 1997

    1997

  5. [13]

    Learning to reason: End-to-end module networks for visual question answering

    Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Kate Saenko. Learning to reason: End-to-end module networks for visual question answering. In Proceedings of the IEEE International Conference on Computer Vision , pages 804– 813, 2017

    2017

  6. [14]

    Compositional Attention Networks for Machine Reasoning

    Drew A Hudson and Christopher D Manning. Compositional Attention Networks for Machine Reasoning. arXiv preprint arXiv:1803.03067, 2018

    2018 arXiv

  7. [15]

    GQA: a new dataset for compositional question answering over real-world images

    Drew A Hudson and Christopher D Manning. GQA: a new dataset for compositional question answering over real-world images. arXiv preprint arXiv:1902.09506, 2019

    1902 arXiv

  8. [16]

    Batch normalization: Accelerating deep network training by reducing internal covariate shift

    Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015

    2015 arXiv

  9. [17]

    CLEVR: A diagnostic dataset for compo- sitional language and elementary visual reasoning

    Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. CLEVR: A diagnostic dataset for compo- sitional language and elementary visual reasoning. In Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recogn...

    2017

  10. [18]

    Adam: A method for stochastic optimization

    Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014

    2014 arXiv

  11. [19]

    AI2-THOR: An Interactive 3D Environment for Visual AI

    Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs, Alvaro Her- rasti, Daniel Gordon, Yuke Zhu, Abhinav Gupta, and Ali Farhadi. AI2-THOR: An Interactive 3D Environment for Visual AI. arXiv, 2017

    2017

  12. [20]

    TVQA: Localized, Composi- tional Video Question Answering

    Jie Lei, Licheng Yu, Mohit Bansal, and Tamara L Berg. TVQA: Localized, Composi- tional Video Question Answering. In EMNLP, 2018. 12 CANGEA, BELILOVSKY , LIÒ, COURVILLE: VIDEONA VQA

    2018

  13. [21]

    Learning visual question answering by bootstrapping hard attention

    Mateusz Malinowski, Carl Doersch, Adam Santoro, and Peter Battaglia. Learning visual question answering by bootstrapping hard attention. In Proceedings of the Eu- ropean Conference on Computer Vision (ECCV), pages 3–20, 2018

    2018

  14. [22]

    Benchmarking Classic and Learned Navigation in Complex 3D Environments

    Dmytro Mishkin, Alexey Dosovitskiy, and Vladlen Koltun. Benchmarking Classic and Learned Navigation in Complex 3D Environments. arXiv preprint arXiv:1901.10915, 2019

    1901 arXiv

  15. [23]

    MarioQA: An- swering Questions by Watching Gameplay Videos

    Jonghwan Mun, Paul Hongsuck Seo, Ilchae Jung, and Bohyung Han. MarioQA: An- swering Questions by Watching Gameplay Videos. In ICCV, 2017

    2017

  16. [24]

    Out of the box: Reasoning with graph convolution nets for factual visual question answering

    Medhini Narasimhan, Svetlana Lazebnik, and Alexander Schwing. Out of the box: Reasoning with graph convolution nets for factual visual question answering. In Ad- vances in Neural Information Processing Systems, pages 2654–2665, 2018

    2018

  17. [25]

    From FiLM to Video: Multi-turn Question Answering with Multi-modal Context

    Dat Tien Nguyen, Shikhar Sharma, Hannes Schulz, and Layla El Asri. From FiLM to Video: Multi-turn Question Answering with Multi-modal Context. arXiv preprint arXiv:1812.07023, 2018

    2018 arXiv

  18. [26]

    Learning conditioned graph structures for interpretable visual question answering

    Will Norcliffe-Brown, Stathis Vafeias, and Sarah Parisot. Learning conditioned graph structures for interpretable visual question answering. In Advances in Neural Informa- tion Processing Systems, pages 8334–8343, 2018

    2018

  19. [27]

    FiLM: Visual reasoning with a general conditioning layer

    Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. FiLM: Visual reasoning with a general conditioning layer. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018

    2018

  20. [28]

    Exploring models and data for im- age question answering

    Mengye Ren, Ryan Kiros, and Richard Zemel. Exploring models and data for im- age question answering. In Advances in neural information processing systems, pages 2953–2961, 2015

    2015

  21. [29]

    Faster R-CNN: Towards real- time object detection with region proposal networks

    Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards real- time object detection with region proposal networks. InAdvances in neural information processing systems, pages 91–99, 2015

    2015

  22. [30]

    Habitat: A Platform for Embodied AI Research

    Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, et al. Habitat: A Platform for Embodied AI Research. arXiv preprint arXiv:1904.01201, 2019

    1904 arXiv

  23. [31]

    Very deep convolutional networks for large- scale image recognition

    Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large- scale image recognition. arXiv preprint arXiv:1409.1556, 2014

    2014 arXiv

  24. [32]

    Semantic Scene Completion from a Single Depth Image

    Shuran Song, Fisher Yu, Andy Zeng, Angel X Chang, Manolis Savva, and Thomas Funkhouser. Semantic Scene Completion from a Single Depth Image. Proceedings of 30th IEEE Conference on Computer Vision and Pattern Recognition, 2017

    2017

  25. [33]

    Visual Reasoning with Multi-hop Feature Modulation

    Florian Strub, Mathieu Seurin, Ethan Perez, Harm De Vries, Jérémie Mary, Philippe Preux, Aaron Courville, and Olivier Pietquin. Visual Reasoning with Multi-hop Feature Modulation. In Proceedings of the European Conference on Computer Vision (ECCV), pages 784–800, 2018. CANGEA,...

    2018

  26. [34]

    MovieQA: Understanding Stories in Movies through Question- Answering

    Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urta- sun, and Sanja Fidler. MovieQA: Understanding Stories in Movies through Question- Answering. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

    2016

  27. [35]

    Graph-structured repre- sentations for visual question answering

    Damien Teney, Lingqiao Liu, and Anton van den Hengel. Graph-structured repre- sentations for visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1–9, 2017

    2017

  28. [36]

    Learning spatiotemporal features with 3D convolutional networks

    Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. Learning spatiotemporal features with 3D convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 4489–4497, 2015

    2015

  29. [37]

    Embodied Question Answering in Photorealistic Environments with Point Cloud Perception

    Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, and Dhruv Batra. Embodied Question Answering in Photorealistic Environments with Point Cloud Perception. In Proceedings of the IEEE Conference on Computer Visi...

    2019

  30. [38]

    Building generalizable agents with a realistic and rich 3D environment

    Yi Wu, Yuxin Wu, Georgia Gkioxari, and Yuandong Tian. Building generalizable agents with a realistic and rich 3D environment. arXiv preprint arXiv:1801.02209 , 2018

    2018 arXiv

  31. [39]

    Stacked attention networks for image question answering

    Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola. Stacked attention networks for image question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 21–29, 2016

    2016

  32. [40]

    Berg, and Dhruv Batra

    Licheng Yu, Xinlei Chen, Georgia Gkioxari, Mohit Bansal, Tamara L. Berg, and Dhruv Batra. Multi-Target Embodied Question Answering. In Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR), 2019. 14 CANGEA, BELILOVSKY , LIÒ, COURVILLE: VIDEONA ...

    2019

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