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REVIEW 4 major objections 4 minor 34 references

Overcoming Semantic Dilution in Transformer-Based Next Frame Prediction

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that splitting a frame embedding into chunks for multi-head attention causes 'semantic dilution' that hurts next-frame prediction, and that feeding the full embedding to every head plus training with an embedding-space…

desk verdict A plausible incremental architecture whose comparative results are confounded by the evaluation metric equaling its own training objective. read the letter →

arxiv 2501.16753 v1 pith:XH27S3D3 submitted 2025-01-28 cs.CV cs.AI

Hy Nguyen , Srikanth Thudumu , Hung Du , Rajesh Vasa , Kon Mouzakis This is my paper · ORCID
classification cs.CVcs.AI
keywords next-framepredictionvideotransformermulti-headself-attentionsemanticdilutionembedding-spacelosssimilarityregularizationSCMHSA
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

Transformer-based next-frame predictors usually split each frame's embedding into chunks, one per attention head, and train with a loss on the reconstructed frame even though the model actually outputs an embedding. This paper argues that both choices hurt prediction, calling the information loss from chunking 'semantic dilution' and the objective/output mismatch a training discrepancy. It proposes SC-VFP, which feeds the complete embedding to every head and trains with an embedding-space loss: MSE between predicted and true next-frame embeddings plus a term that pushes different heads to encode distinct semantics. Across UCSD Pedestrian, UCF Sports, and Penn Action, the paper reports the lowest embedding MSE and highest embedding PSNR among the compared predictors, while on the small KTH dataset the method trails the best baseline. If the comparison holds, the fix matters because it suggests larger, semantically richer video datasets are where attention-head splitting hurts most.

What carries the argument

The load-bearing object is the Semantic Concentration Multi-Head Self-Attention (SCMHSA) block, a drop-in replacement for the standard MHSA block. Instead of partitioning the input embedding into $N$ chunks, SCMHSA gives each head the full embedding and compresses the concatenated outputs with a learnable matrix $W_o$, so semantic information is preserved rather than split. It is trained by a two-term loss: embedding MSE, $\mathrm{MSE}(e_t,\hat{e}_t)$, which aligns the objective with the model's actual embedding output, and semantic-similarity regularization $L_{\mathrm{SS}}$, which averages pairwise row-wise cosine similarity between heads and drives heads to specialize. The machinery works together: full-embedding heads keep the semantics, the regularization prevents the higher-dimensional heads from collapsing into redundant copies, and the embedding-space loss lets the autoregressive predictor be optimized directly where it operates.

What would settle it

Retrain all six baselines with the same embedding-space loss (Eq. 15 plus Eq. 16) and the same frozen ViT encoder used for SC-VFP; if one of them matches or beats SC-VFP on UCSD, UCF Sports, or Penn Action, the claimed superiority is a property of the training metric rather than of SCMHSA. Alternatively, decode SC-VFP's predicted embeddings back to pixels and compute standard pixel-space PSNR/SSIM; if a pixel-trained baseline wins there, the embedding-space gains do not transfer to actual frame quality.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that the standard MHSA block in transformer video-frame predictors is the bottleneck: cutting each embedding $e_t \in \mathbb{R}^d$ into $N$ pieces $e_t^{h_i}\in\mathbb{R}^{d/N}$ strips each head of full context and distorts the latent representation. The proposed Semantic Concentration Multi-Head Self-Attention (SCMHSA) lets every head compute query, key, and value from the whole embedding and then uses a learned projection $W_o$ to fold the concatenated head outputs back to dimension $d$. The second part of the claim is that training should measure error on the predicted embedding $\hat{e}_{M+1}$, not on a decoded frame, using $\mathcal{L}=\mathrm{MSE}(e_{M+1},\hat{e}_{M+1})+\lambda \mathcal{L}_{\mathrm{SS}}$, where the semantic-similarity term $\mathcal{L}_{\mathrm{SS}}$ penalizes pairs of heads whose row-wise cosine similarities are high. The paper reports that this combination gives the lowest embedding MSE and highest embedding PSNR on UCSD Pedestrian ($86.71$, $28.75$), UCF Sports ($79.63$, $29.12$), and Penn Action ($109.92$, $27.72$), and that ablations removing either SCMHSA or the semantic-similarity loss degrade results on the larger datasets, while the small KTH set favors the baseline slightly.

Load-bearing premise

The comparison rests on treating MSE and PSNR computed on ViT [CLS] embeddings as a fair quality measure, even though SC-VFP is trained on embedding MSE while every baseline is trained on pixel-space losses.

Editorial extensions

If this is right

  • If the claim is right, standard transformer predictors should keep the full embedding per head rather than chunking it, since the improvement grows with dataset size: 28.87% MSE reduction over the SCMHSA-less variant on UCSD, 45.29% on UCF Sports, and 35.71% on Penn Action.
  • Training in embedding space with the semantic-similarity term should make transformer next-embedding predictors converge faster and avoid the gradient mismatch that comes from backpropagating through a pixel-space reconstruction loss; the paper shows faster convergence on Penn Action.
  • Embedding-level prediction is enough for downstream uses that care about deviations rather than full pixel detail, such as anomaly detection in object tracking, so SC-VFP's output can feed those tasks directly without a decoder.
  • The benefit of SCMHSA is expected to be largest when training data contains diverse semantics; on small or homogeneous datasets the standard MHSA may remain competitive.
  • The paper's evidence for the semantic-dilution diagnosis is empirical: replacing the splitting operation with full-embedding heads consistently improves embedding-space accuracy on the three larger datasets, while keeping the split and only using the new loss helps less.

Reading between the lines

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

  • A clean test of whether the reported superiority is real rather than metric-driven is to retrain every baseline with the same embedding MSE plus semantic-similarity loss and the same frozen ViT encoder used for SC-VFP; if SC-VFP still wins, the architecture is doing the work.
  • The semantic-similarity term is a decorrelation regularizer, so the same idea could transfer to other multi-head attention settings, such as language or audio transformers, where head redundancy is suspected; this is an extension the paper does not explore.
  • Because the encoder is frozen, jointly fine-tuning the Vision Transformer with SC-VFP could push embedding prediction further; the paper's setup does not test that.
  • The paper does not compare against a version of the baseline transformer that uses the same embedding-space loss without SCMHSA; adding that arm would isolate the contribution of the architectural change from the contribution of the loss change.
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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

4 major / 4 minor

Summary. The paper proposes SC-VFP, a transformer-based video frame predictor that (1) replaces standard multi-head self-attention with SCMHSA, in which each attention head consumes the complete frame embedding rather than a sliced chunk, and (2) trains with an embedding-space loss comprising an MSE term on predicted embeddings and a cosine-similarity term that encourages heads to be diverse. The authors argue that standard MHSA causes 'semantic dilution' and that pixel-space training objectives are misaligned with transformer predictors that output embeddings. Experiments on KTH, UCSD Pedestrian, UCF Sports, and Penn Action report MSE and PSNR computed on ViT [CLS] embeddings, with SC-VFP claimed superior on the three larger datasets. Ablations study the SCMHSA module and the semantic-similarity loss.

Significance. If the central claim were supported, the paper would offer a simple architectural modification (full-embedding attention) and a training-objective alignment for embedding-based video prediction, with potential value for transformer video predictors and for tasks such as anomaly detection where embedding fidelity matters. The paper also provides useful ablations showing that both SCMHSA and the semantic-similarity loss affect performance on larger datasets, and it openly reports a negative result on KTH. However, the headline empirical superiority is not currently demonstrated: the evaluation metric is essentially the proposed method's own training objective, while all external baselines are trained on pixel-space losses, so Table 1 conflates architectural quality with train/evaluation circularity. The semantic-dilution premise is asserted rather than empirically established, and one reported KTH percentage is internally inconsistent. These issues make the state-of-the-art claim indefensible as presented.

major comments (4)
  1. [Section 4.2, Eq. (15), Table 1] The evaluation in Table 1 is not a neutral comparison. SC-VFP is trained with the embedding MSE of Eq. (15), and Section 4.2 evaluates all methods with MSE and PSNR computed on ViT [CLS] embeddings. The external baselines (PredRNN, SA-ConvLSTM, MIMO-VP, LFDM, VFP-ImageEvent, ExtDM) are trained on pixel-space reconstruction losses and output frames, so their embedding scores are obtained post hoc from reconstructed frames. Thus SC-VFP is scored on the metric it directly optimizes, while the baselines are scored on a metric misaligned with their training objectives. The large reported gaps (e.g., 16% to 68% MSE reduction on the three larger datasets) are therefore a plausible artifact of train/evaluation objective matching. The authors should either evaluate all methods with a common pixel-space metric (which requires decoding SC-VFP embeddings to frames), retrain all baselines under the same embedding-space objective, or provide an external embedding-quality benchmark that is not part of SC-VFP's loss.
  2. [Section 4.4.1, Table 1] The KTH result is internally inconsistent. The text states that SC-VFP 'achieved a PSNR score that was 7.01% lower than the best-performing method and an MSE score that was 59.94% lower.' With SC-VFP MSE 79.81 and VFP-ImageEvent MSE 49.9, the SC-VFP MSE is 59.94% higher, not lower. This error obscures the paper's own finding that the proposed method is worse on KTH, and it must be corrected.
  3. [Sections 1 and 3.2] The semantic-dilution premise is asserted without evidence. The paper claims that dividing an embedding into N chunks for MHSA 'distorts the representation of the embedding in the latent space' and 'dilutes semantic information,' but no measurement or analysis is provided to show that this chunking actually degrades semantic content. It is a computational decomposition of the attention operation, not obviously a semantic loss. The authors should quantify semantic dilution, for example by probing the intermediate embeddings on a semantic task, measuring representation similarity, or comparing attention-head redundancy. Without this, the motivating problem and the interpretation of the ablations remain speculative.
  4. [Section 4.2, Figures 3 and 4] The paper does not explain how predicted embeddings are obtained for the baselines, which output frames rather than embeddings. Figure 3 and Figure 4 compare predicted embeddings against ground-truth embeddings for all methods, but the mechanism for extracting an embedding from a baseline's reconstructed frame is never specified. Additionally, the modified embedding PSNR has no stated dynamic range or formula, so the reader cannot assess whether the reported PSNR values are meaningful. These omissions are load-bearing because the entire comparison rests on the validity of the embedding metric.
minor comments (4)
  1. [Section 3.3.2, Eq. (16)] The notation in Eq. (16) is confusing: M is introduced as the length of the input sequence in Section 3.1, but in Eq. (16) it is also the number of rows in each head vector. Please clarify whether these are the same quantity and define the head vectors h_{i,k} precisely.
  2. [Section 4.4, Figure 4] Figure 4's caption contains a typo: 'Consine similarity' should be 'Cosine similarity.'
  3. [Section 4.4.2 and Table 1] In Table 1, MIMO-VP reports identical MSE (154.91) and PSNR (26.23) on KTH and UCSD. Since these datasets differ substantially, please verify whether this is a copying error or an actual coincidence.
  4. [Section 4.1] The preprocessing choice of sampling one frame in every five may break the short-term temporal continuity that several baselines (particularly recurrent models such as PredRNN and SA-ConvLSTM) are designed to exploit. Please discuss whether this choice introduces bias in the comparison.

Circularity Check

1 steps flagged · score 6.0 of 10

Headline superiority is measured on SC-VFP's own training objective: embedding-space MSE/PSNR evaluation confounds the comparison with pixel-trained baselines.

  1. fitted input called prediction [Sec. 3.3.4 (Eq. 15), Sec. 4.2 (Metrics), and Table 1]
    "LMSE = MSE(et, ˆet) =∥et − ˆet∥2 (15) ... Since our approach operates within the embedding space rather than the pixel space, common VFP metrics such as LPIPS [29] and SSIM are not applicable. Instead, we employ PSNR and MSE as our evaluation metrics. While PSNR is conventionally calculated using the pixel values, we modify it to utilize the embedding values."

    SC-VFP is trained to minimize embedding MSE (Eq. 15), and Table 1 ranks all methods by MSE and PSNR computed on ViT [CLS] embeddings (Sec. 4.2). The evaluation metric is therefore exactly SC-VFP's own training objective. The baselines (PredRNN, SA-ConvLSTM, MIMO-VP, LFDM, VFP-ImageEvent, ExtDM) are trained with pixel-space reconstruction losses and are evaluated post hoc by encoding their reconstructed frames with the same frozen ViT; their objectives are not aligned with the embedding metric. The reported large margins (e.g., 16.14%, 38.3%, and 68.71% MSE reductions on UCSD, UCF Sports, and Penn Action) thus largely measure how well SC-VFP optimizes the test metric while the baselines optimize a different objective.

full rationale

The architectural derivation itself is not circular: SCMHSA feeds the complete embedding to each head, adds a projection W_o, and uses a semantic-similarity regularizer; this is a stated design change with an ablation study. I found no load-bearing self-citations; the cited prior work is external and the method is evaluated against published baselines rather than derived from the authors' own theorems. The circularity burden is concentrated in the evaluation design. Equation 15 defines the training loss, and Section 4.2 defines the evaluation metrics as MSE and PSNR computed on the same embedding space. Because every external baseline is trained with pixel-space losses and only later encoded into embeddings for scoring, the comparison metric is aligned with SC-VFP's objective but misaligned with the baselines' objectives. The KTH result, where SC-VFP does not win, shows the effect is not strictly tautological, but the central claim on the three larger datasets remains confounded by train/eval objective identity. That is a partial, substantive circularity rather than a minor citation issue, so the score is 6.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central claims rest on one hand-picked hyperparameter (lambda) and several unverified domain assumptions: that MHSA splitting causes a measurable semantic dilution, that the ViT [CLS] token is a sufficient frame representation for prediction, that penalizing head similarity produces complementary semantics, and that embedding-space metrics are valid for comparing models with different training objectives. No new physical entities are introduced. The claims are empirical and are not derived from first principles.

free parameters (1)
  • lambda (semantic similarity weight) = not reported
    Hyperparameter in Eq. 17 balancing embedding MSE and L_SS; chosen by hand, value not reported in the paper.
assumptions (4)
  • domain assumption Multi-head self-attention splitting of the embedding causes semantic dilution
    Central premise of the paper, asserted in Sec. 1 and Sec. 3.3.2 without formal proof or quantitative measurement.
  • domain assumption The [CLS] token from ViT provides a sufficient frame representation for next-frame prediction
    Used in Sec. 3.3.1 to define frame embeddings; not empirically validated for this task.
  • domain assumption Penalizing pairwise cosine similarity of head outputs encourages heads to learn distinct useful semantics
    Core design of SSL loss in Sec. 3.3.4; no evidence that diversity in this sense equals complementarity in predictive value.
  • domain assumption Embedding-space MSE/PSNR is a valid proxy for next-frame prediction quality
    Sec. 4.2 modifies PSNR to use embedding values; no validation against pixel-space reconstruction quality.

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Cite this review

Pith. "Pith review of Overcoming Semantic Dilution in Transformer-Based Next Frame Prediction." pith.science (2026). https://pith.science/paper/XH27S3D3

@misc{pith2026250116753,
  author       = {Pith},
  title        = {Pith review of: Overcoming Semantic Dilution in Transformer-Based Next Frame Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XH27S3D3}},
  note         = {Machine review of arXiv:2501.16753}
}
abstract

Next-frame prediction in videos is crucial for applications such as autonomous driving, object tracking, and motion prediction. The primary challenge in next-frame prediction lies in effectively capturing and processing both spatial and temporal information from previous video sequences. The transformer architecture, known for its prowess in handling sequence data, has made remarkable progress in this domain. However, transformer-based next-frame prediction models face notable issues: (a) The multi-head self-attention (MHSA) mechanism requires the input embedding to be split into $N$ chunks, where $N$ is the number of heads. Each segment captures only a fraction of the original embeddings information, which distorts the representation of the embedding in the latent space, resulting in a semantic dilution problem; (b) These models predict the embeddings of the next frames rather than the frames themselves, but the loss function based on the errors of the reconstructed frames, not the predicted embeddings -- this creates a discrepancy between the training objective and the model output. We propose a Semantic Concentration Multi-Head Self-Attention (SCMHSA) architecture, which effectively mitigates semantic dilution in transformer-based next-frame prediction. Additionally, we introduce a loss function that optimizes SCMHSA in the latent space, aligning the training objective more closely with the model output. Our method demonstrates superior performance compared to the original transformer-based predictors.

Figures

Figures reproduced from arXiv: 2501.16753 by the authors.

Figure 1
Figure 1. Multi-head Self-attention (MHSA) block in VFP divides [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of our proposed network model. We first extract embeddings from [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Qualitative comparison of methods on an example frame from KTH dataset. The top shows the error map of the predicted [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Consine similarity between the first five predicted em [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Convergence speed of SC-VFP with and without [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

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Reference graph

Works this paper leans on

34 extracted references · 27 canonical work pages

  1. [1]

    Vivit: A video vision transformer

    Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lu ˇci´c, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF inter- national conference on computer vision , pages 6836–6846,

  2. [2]

    The problem of learning long-term dependencies in recurrent net- works

    Yoshua Bengio, Paolo Frasconi, and Patrice Simard. The problem of learning long-term dependencies in recurrent net- works. In IEEE international conference on neural networks, pages 1183–1188. IEEE, 1993. 1

    work page 1993

  3. [3]

    Mau: A motion- aware unit for video prediction and beyond

    Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xiang Xinguang, and Wen Gao. Mau: A motion- aware unit for video prediction and beyond. Advances in Neural Information Processing Systems , 34:26950–26962,

  4. [4]

    Two deterministic half-quadratic regular- ization algorithms for computed imaging

    Pierre Charbonnier, Laure Blanc-Feraud, Gilles Aubert, and Michel Barlaud. Two deterministic half-quadratic regular- ization algorithms for computed imaging. In Proceedings of 1st international conference on image processing , pages 168–172. IEEE, 1994. 1

    work page 1994

  5. [5]

    An image is worth 16x16 words: Trans- formers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, et al. An image is worth 16x16 words: Trans- formers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 4, 5, 6

    arXiv 2010

  6. [6]

    Anticipative video transformer

    Rohit Girdhar and Kristen Grauman. Anticipative video transformer. In Proceedings of the IEEE/CVF international conference on computer vision , pages 13505–13515, 2021. 1, 4

    work page 2021

  7. [7]

    Human action recognition and predic- tion: A survey

    Yu Kong and Yun Fu. Human action recognition and predic- tion: A survey. International Journal of Computer Vision , 130(5):1366–1401, 2022. 1

    2022

  8. [8]

    Self-attention convlstm for spatiotemporal prediction

    Zhihui Lin, Maomao Li, Zhuobin Zheng, Yangyang Cheng, and Chun Yuan. Self-attention convlstm for spatiotemporal prediction. In Proceedings of the AAAI conference on artifi- cial intelligence, pages 11531–11538, 2020. 1, 3, 4, 6

    work page 2020

Show all 34 references
  1. [9]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6

    2017 arXiv

  2. [10]

    Future frame prediction using convolutional vrnn for anomaly detection

    Yiwei Lu, K Mahesh Kumar, Seyed shahabeddin Nabavi, and Yang Wang. Future frame prediction using convolutional vrnn for anomaly detection. In2019 16th IEEE International Conference on Advanced Video and Signal Based Surveil- lance (AVSS), pages 1–8. IEEE, 2019. 1

    2019

  3. [11]

    Anomaly detection in crowded scenes

    Vijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vas- concelos. Anomaly detection in crowded scenes. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1975–1981, 2010. 2, 6

    2010

  4. [12]

    A hybrid transformer-lstm model with 3d separable convolution for video prediction

    Mareeta Mathai, Ying Liu, and Nam Ling. A hybrid transformer-lstm model with 3d separable convolution for video prediction. IEEE Access, 2024. 3

    2024

  5. [13]

    Deep multi-scale video prediction beyond mean square error

    Michael Mathieu, Camille Couprie, and Yann LeCun. Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440, 2015. 1

    2015 arXiv

  6. [14]

    Unflow: Un- supervised learning of optical flow with a bidirectional cen- sus loss

    Simon Meister, Junhwa Hur, and Stefan Roth. Unflow: Un- supervised learning of optical flow with a bidirectional cen- sus loss. In Proceedings of the AAAI conference on artificial intelligence, 2018. 1

    2018

  7. [15]

    Conditional image-to-video gener- ation with latent flow diffusion models

    Haomiao Ni, Changhao Shi, Kai Li, Sharon X Huang, and Martin Renqiang Min. Conditional image-to-video gener- ation with latent flow diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18444–18455, 2023. 6

    2023

  8. [16]

    Mimo is all you need: A strong multi-in-multi-out base- line for video prediction

    Shuliang Ning, Mengcheng Lan, Yanran Li, Chaofeng Chen, Qian Chen, Xunlai Chen, Xiaoguang Han, and Shuguang Cui. Mimo is all you need: A strong multi-in-multi-out base- line for video prediction. In Proceedings of the AAAI confer- ence on artificial intelligence, pages 1975–19...

    1975

  9. [17]

    Recog- nizing human actions: a local svm approach

    Christian Schuldt, Ivan Laptev, and Barbara Caputo. Recog- nizing human actions: a local svm approach. In Proceedings of the 17th International Conference on Pattern Recognition,

  10. [18]

    Convolutional lstm network: A machine learning approach for precipitation nowcasting

    Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in neural information processing sys- tems, 28, 2015. 2

    2015

  11. [19]

    Deep learning for precipitation nowcasting: A benchmark and a new model

    Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit- Yan Yeung, Wai-kin Wong, and Wang-chun Woo. Deep learning for precipitation nowcasting: A benchmark and a new model. Advances in neural information processing sys- tems, 30, 2017. 2

    2017

  12. [20]

    Action recognition in realistic sports videos

    Khurram Soomro and Amir R Zamir. Action recognition in realistic sports videos. In Computer vision in sports , pages 181–208. Springer, 2015. 2, 6

    2015

  13. [21]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszko- reit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 1, 2

    2017

  14. [22]

    Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms

    Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, and Philip S Yu. Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. Advances in neural information processing systems, 30, 2017. 1, 2, 6

    2017

  15. [23]

    Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learn- ing

    Yunbo Wang, Zhifeng Gao, Mingsheng Long, Jianmin Wang, and S Yu Philip. Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learn- ing. In International conference on machine learning, pages 5123–5132. PMLR, 2018. 2

    2018

  16. [24]

    Eidetic 3d lstm: A model for video prediction and beyond

    Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Ming- sheng Long, and Li Fei-Fei. Eidetic 3d lstm: A model for video prediction and beyond. In International conference on learning representations, 2018. 1

    2018

  17. [25]

    Memory in memory: A predictive neural network for learning higher-order non- stationarity from spatiotemporal dynamics

    Yunbo Wang, Jianjin Zhang, Hongyu Zhu, Mingsheng Long, Jianmin Wang, and Philip S Yu. Memory in memory: A predictive neural network for learning higher-order non- stationarity from spatiotemporal dynamics. In Proceedings of the IEEE/CVF conference on computer vision and patter...

    2019

  18. [26]

    Motionrnn: A flexible model for video predic- tion with spacetime-varying motions

    Haixu Wu, Zhiyu Yao, Jianmin Wang, and Mingsheng Long. Motionrnn: A flexible model for video predic- tion with spacetime-varying motions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15435–15444, 2021. 1

    2021

  19. [27]

    Mo- tionnet: Joint perception and motion prediction for au- tonomous driving based on bird’s eye view maps

    Pengxiang Wu, Siheng Chen, and Dimitris N Metaxas. Mo- tionnet: Joint perception and motion prediction for au- tonomous driving based on bird’s eye view maps. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11385–11395, 2020. 1

    2020

  20. [28]

    Vptr: Efficient transformers for video prediction

    Xi Ye and Guillaume-Alexandre Bilodeau. Vptr: Efficient transformers for video prediction. In2022 26th International Conference on Pattern Recognition (ICPR) , pages 3492–

  21. [29]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 586–595, 2018. 1, 6

    2018

  22. [30]

    From actemes to action: A strongly-supervised representa- tion for detailed action understanding

    Weiyu Zhang, Menglong Zhu, and Konstantinos G Derpanis. From actemes to action: A strongly-supervised representa- tion for detailed action understanding. In Proceedings of the IEEE international conference on computer vision, pages 2248–2255, 2013. 2, 6

    2013

  23. [31]

    Extdm: Distribution extrapolation dif- fusion model for video prediction

    Zhicheng Zhang, Junyao Hu, Wentao Cheng, Danda Paudel, and Jufeng Yang. Extdm: Distribution extrapolation dif- fusion model for video prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19310–19320, 2024. 6

    2024

  24. [32]

    Video frame prediction from a single image and events

    Juanjuan Zhu, Zhexiong Wan, and Yuchao Dai. Video frame prediction from a single image and events. InProceedings of the AAAI Conference on Artificial Intelligence, pages 7748– 7756, 2024. 1, 6

    2024

  25. [2004]

    IEEE, 2004

    ICPR 2004., pages 32–36. IEEE, 2004. 2, 6

    2004

  26. [3499]

    1, 2, 3, 4

    IEEE, 2022. 1, 2, 3, 4

    2022

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