pith. sign in

arxiv: 2607.00858 · v1 · pith:OMG4EXE2new · submitted 2026-07-01 · 💻 cs.CV · cs.LG

MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment

Peiyuan Zhu , Shaoan Xie , Zijian Li , Yifan Shen , Namrata Deka , Harsh Shrivastava , Guangyi Chen , Kun Zhang This is my paper

Pith reviewed 2026-07-02 14:22 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords video-text alignmentasymmetric projectionstemporal misalignmentsemantic asymmetrymodular alignmentcontrastive learninglong video understandingframe disentanglement
0
0 comments X

The pith

Dual asymmetric projections align long videos with texts by separating global semantics from frame-specific details.

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

The paper establishes theoretical conditions allowing flexible video-text alignment across time and levels of detail. It introduces MoVA to learn a text projection that picks frame-relevant parts of a caption and a video projection that isolates text-related visual elements. This keeps overall cross-modal meaning intact while avoiding entanglement of static objects with their changes over time. A sympathetic reader would care because standard contrastive models fail when text matches only some frames or when captions mix static and dynamic concepts.

Core claim

MoVA learns dual asymmetric projections: a text-side projection that adaptively selects frame-aware subspaces of the caption and a video-side projection that disentangles text-relevant visual concepts, based on conditions for alignment across temporal dimensions and granularity, so that global semantics are preserved while frame-specific concepts are separated and the approach scales to long videos and captions.

What carries the argument

Dual asymmetric projections, with the text projection selecting frame-aware caption subspaces and the video projection disentangling relevant visual concepts.

If this is right

  • Global cross-modal semantics remain preserved while frame-specific concepts are disentangled.
  • The method scales naturally to long captions and videos without increased entanglement.
  • Performance improves on multiple video-text alignment tasks compared with prior methods.
  • Text-irrelevant frames can be handled without forcing full-caption equivalence.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar dual-projection logic could be tested on tasks like video question answering where only portions of a clip are relevant.
  • The separation of global and local concepts might reduce error propagation in downstream video generation models.
  • Applying the asymmetry handling to multi-shot or multi-scene videos could expose whether the projections generalize beyond single continuous clips.

Load-bearing premise

Theoretical conditions exist that enable flexible alignment between video and text representations across the temporal dimension and at varying levels of granularity.

What would settle it

A controlled test on video-text pairs with known temporal mismatch and semantic asymmetry where MoVA shows no accuracy gain over standard contrastive baselines would falsify the claim.

Figures

Figures reproduced from arXiv: 2607.00858 by Guangyi Chen, Harsh Shrivastava, Kun Zhang, Namrata Deka, Peiyuan Zhu, Shaoan Xie, Yifan Shen, Zijian Li.

Figure 1
Figure 1. Figure 1: Illustration of two key challenges for video–language alignment. (1) Temporal Misalignment: Video captions are inherently temporally misaligned with the under￾lying visual content. Multiple paired captions emphasize different moments along the video timeline—the first frame focuses on the man talking, while the second centers on the table tennis game. A single caption may describe an action occurring withi… view at source ↗
Figure 2
Figure 2. Figure 2: The data-generating process. The video-text pair originates from its cor￾responding representation pair. The mapping from the text representation z T to the sequence of frame representations z V over time is sparse. The subset of text represen￾tations associated with a particular frame representation contains only partial infor￾mation about the vision representation. objective is to learn high-level semant… view at source ↗
Figure 3
Figure 3. Figure 3: MoVA overview. We integrate dual asymmetric projections: the Temporal Mask Network selects which subset of the global text representation to be used, while the Concept Mask Network selects the parts of the frame representation that align with the selected, correlated text. For example, repeated mentions of the same actor or action can be isolated by intersecting the corresponding masks, which explains the … view at source ↗
Figure 4
Figure 4. Figure 4: A test example on ActivityNet illustrating the most relevant text subsets and weights assigned by the Temporal Mask Network to each frame (top-3 only). A black Labrador in a snug dark leather jacket sits in a small, round, worn wooden coracle on green-brown water. Two matte metal mugs lie on the coracle’s boards, and the dog picks up one to sip. The Labrador turns its head to one side, scanning the surroun… view at source ↗
Figure 5
Figure 5. Figure 5: Example of Long-text-to-video generation. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of learned concepts. We perform representation visualization by formulating a proxy classification task. The cosine similarity between frame embeddings across time and a generated caption (e.g., “some pigs”) serves as the classification score for GradCAM [41] attribution. Red boxes mark the temporally active regions. (a) (b) (c) (d) [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: (a) Scaling with epochs on ActivityNet; (b) Scaling with epochs on UltraVideo; (c) Scaling trends across ViT backbones; (d) Ablation on loss-coefficient ratio λg/λtv. ume and model size, which is essential for the long-form scenarios highlighted in the Introduction. Loss weighting [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Additional Comparison Examples of Long-text-to-video generation. [PITH_FULL_IMAGE:figures/full_fig_p029_8.png] view at source ↗
read the original abstract

Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLIP, resulting in entangled representations. These challenges are severely exacerbated by two fundamental properties in the video domain: Temporal Misalignment, where textual descriptions often correlate only to specific, constrained temporal windows, leaving other frames text-irrelevant; and Semantic Asymmetry, which dictates a sparse, bidirectional, and non-equivalent relevance between frame-level visual details and caption-level concepts. This failure persists whether captions are short and temporally disjoint, creating ambiguity, or long and detailed, fostering entanglement between static objects and their temporal evolution. In this paper, we establish theoretical conditions that enable flexible alignment between video and text representations across the temporal dimension and at varying levels of granularity. Building on these theoretical insights, we introduce MoVA, Modular Long Video-Text Alignment, which learns dual asymmetric projections: a text-side projection that adaptively selects frame-aware subspaces of the caption, and a video-side projection that disentangles text-relevant visual concepts. Our framework ensures that the model can preserve global cross-modal semantics while disentangling evolving, frame-specific concepts and scale naturally to long captions and videos. Empirical evaluations show that MoVA outperforms existing methods in multiple video-text alignment tasks, demonstrating the effectiveness of our method.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper claims that temporal misalignment and semantic asymmetry in video-text data can be addressed by first establishing (unspecified) theoretical conditions for flexible cross-modal alignment across time and granularity, then introducing MoVA, which learns a text-side projection that selects frame-aware caption subspaces and a video-side projection that disentangles text-relevant visual concepts; the resulting model is said to preserve global semantics while scaling to long videos/captions and to outperform prior methods on multiple alignment tasks.

Significance. If the asserted theoretical conditions can be shown to exist, be non-trivial, and directly motivate the dual asymmetric projections, and if the empirical gains are reproducible with proper controls, the work could provide a modular way to mitigate entanglement issues inherited from image-text models like CLIP when applied to video. No machine-checked proofs, parameter-free derivations, or falsifiable predictions are visible in the supplied abstract.

major comments (2)
  1. [Abstract] Abstract: the central motivation rests on the existence of 'theoretical conditions that enable flexible alignment between video and text representations across the temporal dimension and at varying levels of granularity,' yet the abstract supplies neither equations, assumptions, nor a proof sketch; without these the necessity of the dual asymmetric projections for handling Temporal Misalignment and Semantic Asymmetry cannot be evaluated.
  2. [Abstract] Abstract: the claim that 'MoVA outperforms existing methods in multiple video-text alignment tasks' is presented without any dataset names, baseline comparisons, quantitative metrics, error bars, or ablation results, rendering it impossible to assess whether the reported gains are attributable to the claimed mechanism rather than implementation details.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their comments on the manuscript. We respond point-by-point to the major comments below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central motivation rests on the existence of 'theoretical conditions that enable flexible alignment between video and text representations across the temporal dimension and at varying levels of granularity,' yet the abstract supplies neither equations, assumptions, nor a proof sketch; without these the necessity of the dual asymmetric projections for handling Temporal Misalignment and Semantic Asymmetry cannot be evaluated.

    Authors: We agree the abstract omits the mathematical details due to length constraints. The theoretical conditions, including assumptions on temporal and granularity alignment and the derivation motivating the asymmetric projections, are established in Section 3 of the full manuscript. To address the concern, we will revise the abstract to briefly reference the key theoretical result. revision: yes

  2. Referee: [Abstract] Abstract: the claim that 'MoVA outperforms existing methods in multiple video-text alignment tasks' is presented without any dataset names, baseline comparisons, quantitative metrics, error bars, or ablation results, rendering it impossible to assess whether the reported gains are attributable to the claimed mechanism rather than implementation details.

    Authors: Abstracts are space-limited and provide summaries; full details on datasets, baselines, metrics, error bars, and ablations appear in Sections 4 and 5. We will partially revise the abstract to name representative tasks and note the scale of gains while keeping within length limits. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The abstract asserts theoretical conditions enabling flexible video-text alignment and introduces dual asymmetric projections as a consequence, but supplies no equations, fitted parameters, or derivation steps that reduce to self-defined quantities or self-citations. Claims rest on empirical outperformance rather than a closed loop; the reader's assessment of score 2.0 aligns with absence of inspectable circular reductions in the visible text. The framework is presented as building on stated insights without the patterns of self-definition or imported uniqueness.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract mentions theoretical conditions but lists no free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5785 in / 894 out tokens · 25653 ms · 2026-07-02T14:22:33.719056+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

77 extracted references · 26 canonical work pages · 5 internal anchors

  1. [1]

    In: Proceedings of the IEEE international conference on computer vision

    Anne Hendricks, L., Wang, O., Shechtman, E., Sivic, J., Darrell, T., Russell, B.: Localizing moments in video with natural language. In: Proceedings of the IEEE international conference on computer vision. pp. 5803–5812 (2017)

    2017

  2. [2]

    Qwen2.5-VL Technical Report

    Bai, S., Chen, K., Liu, X., Wang, J., Ge, W., Song, S., Dang, K., Wang, P., Wang, S., Tang, J., et al.: Qwen2.5-vl technical report. arXiv preprint arXiv:2502.13923 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  3. [3]

    Journal of machine learning research3(Feb), 1137–1155 (2003)

    Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. Journal of machine learning research3(Feb), 1137–1155 (2003)

    2003

  4. [4]

    Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

    Bengio, Y., Léonard, N., Courville, A.: Estimating or propagating gradi- ents through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013) 16 P. Zhu et al

    work page internal anchor Pith review Pith/arXiv arXiv 2013

  5. [5]

    In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies

    Chen, D., Dolan, W.B.: Collecting highly parallel data for paraphrase evaluation. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies. pp. 190–200 (2011)

    2011

  6. [6]

    Advances in Neural Information Processing Systems38(2026)

    Chen,G., Deng, Y.,Zhu,P., Li,Y., Shen, Y.,Li, Z., Zhang,K.: Causalverse: Bench- marking causal representation learning with configurable high-fidelity simulations. Advances in Neural Information Processing Systems38(2026)

    2026

  7. [7]

    In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Chen, H., Zhang, Y., Cun, X., Xia, M., Wang, X., Weng, C., Shan, Y.: Videocrafter2: Overcoming data limitations for high-quality video diffusion mod- els. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7310–7320 (2024).https://doi.org/10.1109/CVPR52733.2024. 00698

    work page doi:10.1109/cvpr52733.2024 2024

  8. [8]

    In: Eu- ropean Conference on Computer Vision

    Chen, L., Li, J., Dong, X., Zhang, P., He, C., Wang, J., Zhao, F., Lin, D.: Sharegpt4v: Improving large multi-modal models with better captions. In: Eu- ropean Conference on Computer Vision. pp. 370–387. Springer (2024).https: //doi.org/10.1007/978-3-031-72643-9_22

    work page doi:10.1007/978-3-031-72643-9_22 2024

  9. [9]

    Advances in Neural Information Processing Systems36, 72842–72866 (2023)

    Chen,S.,Li,H.,Wang,Q.,Zhao,Z.,Sun,M.,Zhu,X.,Liu,J.:Vast:Avision-audio- subtitle-text omni-modality foundation model and dataset. Advances in Neural Information Processing Systems36, 72842–72866 (2023)

    2023

  10. [10]

    In: International conference on machine learning

    Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for con- trastive learning of visual representations. In: International conference on machine learning. pp. 1597–1607. PmLR (2020)

    2020

  11. [11]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Chen, Y., Chen, D., Liu, R., Zhou, S., Xue, W., Peng, W.: Align before adapt: Leveraging entity-to-region alignments for generalizable video action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18688–18698 (2024)

    2024

  12. [12]

    Proceedings of the AAAI Conference on Artificial Intelligence37(1), 396–404 (2023).https://doi.org/10

    Chen, Y., Wang, J., Lin, L., Qi, Z., Ma, J., Shan, Y.: Tagging before alignment: Integrating multi-modal tags for video-text retrieval. Proceedings of the AAAI Conference on Artificial Intelligence37(1), 396–404 (2023).https://doi.org/10. 1609/aaai.v37i1.25113

    2023

  13. [13]

    arXiv preprint arXiv:2109.04290 (2021)

    Cheng, X., Lin, H., Wu, X., Yang, F., Shen, D.: Improving video-text re- trieval by multi-stream corpus alignment and dual softmax loss. arXiv preprint arXiv:2109.04290 (2021)

    work page arXiv 2021

  14. [14]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Croitoru, I., Bogolin, S.V., Leordeanu, M., Jin, H., Zisserman, A., Albanie, S., Liu, Y.: Teachtext: Crossmodal generalized distillation for text-video retrieval. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 11583–11593 (2021)

    2021

  15. [15]

    arXiv preprint arXiv:2303.09166 (2023)

    Daunhawer, I., Bizeul, A., Palumbo, E., Marx, A., Vogt, J.E.: Identifiability results for multimodal contrastive learning. arXiv preprint arXiv:2303.09166 (2023)

    work page arXiv 2023

  16. [16]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Deng, C., Chen, Q., Qin, P., Chen, D., Wu, Q.: Prompt switch: Efficient clip adaptation for text-video retrieval. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 15648–15658 (2023)

    2023

  17. [17]

    Advances in Neural Information Processing Systems36, 35544– 35575 (2023)

    Fan, L., Krishnan, D., Isola, P., Katabi, D., Tian, Y.: Improving clip training with language rewrites. Advances in Neural Information Processing Systems36, 35544– 35575 (2023)

    2023

  18. [18]

    In: Uncertainty in Artificial Intelligence

    Gresele, L., Rubenstein, P.K., Mehrjou, A., Locatello, F., Schölkopf, B.: The in- complete rosetta stone problem: Identifiability results for multi-view nonlinear ica. In: Uncertainty in Artificial Intelligence. pp. 217–227. PMLR (2020)

    2020

  19. [19]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Huang, Z., He, Y., Yu, J., Zhang, F., Si, C., Jiang, Y., Zhang, Y., Wu, T., Jin, Q., Chanpaisit, N., et al.: Vbench: Comprehensive benchmark suite for video gener- ative models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21807–21818 (2024) MoVA: Modular Long Video–Text Alignment 17

    2024

  20. [20]

    In: Artificial intelligence and statistics

    Hyvarinen, A., Morioka, H.: Nonlinear ica of temporally dependent stationary sources. In: Artificial intelligence and statistics. pp. 460–469. PMLR (2017)

    2017

  21. [21]

    IEEE Transactions on Circuits and Systems for Video Technology36(3), 3185–3200 (2026).https:// doi.org/10.1109/TCSVT.2025.3616201

    Jiang, W., Guan, W., Li, H., Li, Z., Fang, Y., Peng, Y., Zhao, X., Liu, Y.: Text- conditional visual-language alignment for video captioning. IEEE Transactions on Circuits and Systems for Video Technology36(3), 3185–3200 (2026).https:// doi.org/10.1109/TCSVT.2025.3616201

    work page doi:10.1109/tcsvt.2025.3616201 2026

  22. [22]

    In: Proceedings of the Thirty-Second International Joint Conference on Artificial In- telligence

    Jin, P., Li, H., Cheng, Z., Huang, J., Wang, Z., Yuan, L., Liu, C., Chen, J.: Text- video retrieval with disentangled conceptualization and set-to-set alignment. In: Proceedings of the Thirty-Second International Joint Conference on Artificial In- telligence. pp. 938–946 (2023).https://doi.org/10.24963/ijcai.2023/104

    work page doi:10.24963/ijcai.2023/104 2023

  23. [23]

    Learning Zero-Shot Subject-Driven Video Generation Using 1% Compute

    Kim, D., Zhang, J., Jin, W., Cho, S., Dai, Q., Park, J., Luo, C.: Subject- driven video generation via disentangled identity and motion. arXiv preprint arXiv:2504.17816 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  24. [24]

    arXiv preprint arXiv:2007.10930 (2020)

    Klindt, D., Schott, L., Sharma, Y., Ustyuzhaninov, I., Brendel, W., Bethge, M., Paiton, D.: Towards nonlinear disentanglement in natural data with temporal sparse coding. arXiv preprint arXiv:2007.10930 (2020)

    work page arXiv 2007

  25. [25]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Ko, D., Choi, J., Ko, J., Noh, S., On, K.W., Kim, E.S., Kim, H.J.: Video-text representation learning via differentiable weak temporal alignment. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5016–5025 (2022)

    2022

  26. [26]

    In: Proceedings of the IEEE international conference on computer vision

    Krishna, R., Hata, K., Ren, F., Fei-Fei, L., Carlos Niebles, J.: Dense-captioning events in videos. In: Proceedings of the IEEE international conference on computer vision. pp. 706–715 (2017)

    2017

  27. [27]

    In: European Conference on Computer Vision

    Lai, Z., Zhang, H., Zhang, B., Wu, W., Bai, H., Timofeev, A., Du, X., Gan, Z., Shan, J., Chuah, C.N., et al.: Veclip: Improving clip training via visual-enriched captions. In: European Conference on Computer Vision. pp. 111–127. Springer (2024)

    2024

  28. [28]

    In: Findings of the AssociationforComputationalLinguistics:EACL2024.pp.1487–1500.Association for Computational Linguistics, St

    Lewis, M., Nayak, N., Yu, P., Merullo, J., Yu, Q., Bach, S., Pavlick, E.: Does CLIP bind concepts? probing compositionality in large image models. In: Findings of the AssociationforComputationalLinguistics:EACL2024.pp.1487–1500.Association for Computational Linguistics, St. Julian’s, Malta (2024).https://doi.org/10. 18653/v1/2024.findings-eacl.101

    2024

  29. [29]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Li, P., Xie, C.W., Zhao, L., Xie, H., Ge, J., Zheng, Y., Zhao, D., Zhang, Y.: Progres- sive spatio-temporal prototype matching for text-video retrieval. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4100–4110 (2023)

    2023

  30. [30]

    Neuro- computing508, 293–304 (2022)

    Luo, H., Ji, L., Zhong, M., Chen, Y., Lei, W., Duan, N., Li, T.: Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning. Neuro- computing508, 293–304 (2022)

    2022

  31. [31]

    In: Proceedings of the 30th ACM international conference on multimedia

    Ma, Y., Xu, G., Sun, X., Yan, M., Zhang, J., Ji, R.: X-clip: End-to-end multi- grained contrastive learning for video-text retrieval. In: Proceedings of the 30th ACM international conference on multimedia. pp. 638–647 (2022)

    2022

  32. [32]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Materzyńska, J., Torralba, A., Bau, D.: Disentangling visual and written concepts in clip. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16410–16419 (2022)

    2022

  33. [33]

    arXiv preprint arXiv:2310.15709 , year=

    Morioka, H., Hyvärinen, A.: Causal representation learning made identifiable by grouping of observational variables. arXiv preprint arXiv:2310.15709 (2023)

    work page arXiv 2023

  34. [34]

    In: International con- ference on artificial intelligence and statistics

    Morioka, H., Hyvarinen, A.: Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data. In: International con- ference on artificial intelligence and statistics. pp. 3399–3426. PMLR (2023) 18 P. Zhu et al

    2023

  35. [35]

    Representation Learning with Contrastive Predictive Coding

    Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predic- tive coding. arXiv preprint arXiv:1807.03748 (2018)

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  36. [36]

    In: International Conference on Learning Representations (2021)

    Patrick, M., Huang, P.Y., Asano, Y., Metze, F., Hauptmann, A.G., Henriques, J.F., Vedaldi, A.: Support-set bottlenecks for video-text representation learning. In: International Conference on Learning Representations (2021)

    2021

  37. [37]

    In: International conference on machine learning

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PmLR (2021)

    2021

  38. [38]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Rasheed, H., Khattak, M.U., Maaz, M., Khan, S., Khan, F.S.: Fine-tuned clip models are efficient video learners. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 6545–6554 (2023)

    2023

  39. [39]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Rizve, M.N., Fei, F., Unnikrishnan, J., Tran, S., Yao, B.Z., Zeng, B., Shah, M., Chilimbi, T.: Vidla: Video-language alignment at scale. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14043– 14055 (2024)

    2024

  40. [40]

    IEEE transactions on acoustics, speech, and signal processing 26(1), 43–49 (1978).https://doi.org/10.1109/TASSP.1978.1163055

    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE transactions on acoustics, speech, and signal processing 26(1), 43–49 (1978).https://doi.org/10.1109/TASSP.1978.1163055

    work page doi:10.1109/tassp.1978.1163055 1978

  41. [41]

    In: Proceedings of the IEEE International Conference on Computer Vision

    Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad- cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 618– 626 (2017)

    2017

  42. [42]

    In: The Fourteenth International Conference on Learning Representations (2026)

    Shen, Y., Zhu, P., Li, Z., Xie, S., Deka, N., Liu, Z., Tang, Z., Chen, G., Zhang, K.: Controllable video generation with provable disentanglement. In: The Fourteenth International Conference on Learning Representations (2026)

    2026

  43. [43]

    ACM Transactions on Multimedia Comput- ing, Communications and Applications19(2), 1–21 (2023)

    Shi, Y., Xu, H., Yuan, C., Li, B., Hu, W., Zha, Z.J.: Learning video-text aligned representations for video captioning. ACM Transactions on Multimedia Comput- ing, Communications and Applications19(2), 1–21 (2023)

    2023

  44. [44]

    arXiv preprint arXiv:2411.06518 (2025)

    Sun, Y., Kong, L., Chen, G., Li, L., Luo, G., Li, Z., Zhang, Y., Zheng, Y., Yang, M., Stojanov, P., Segal, E., Xing, E.P., Zhang, K.: Causal representation learning from multimodal biomedical observations. arXiv preprint arXiv:2411.06518 (2025)

    work page arXiv 2025

  45. [45]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition

    Thrush, T., Jiang, R., Bartolo, M., Singh, A., Williams, A., Kiela, D., Ross, C.: Winoground: Probing vision and language models for visio-linguistic composition- ality. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition. pp. 5238–5248 (2022)

    2022

  46. [46]

    In: Proceedings of the AAAI conference on artificial intelligence

    Tian, K., Cheng, Y., Liu, Y., Hou, X., Chen, Q., Li, H.: Towards efficient and effective text-to-video retrieval with coarse-to-fine visual representation learning. In: Proceedings of the AAAI conference on artificial intelligence. vol. 38, pp. 5207– 5214 (2024)

    2024

  47. [47]

    Advances in neural information processing systems34, 16451–16467 (2021)

    Von Kügelgen, J., Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., Locatello, F.: Self-supervised learning with data augmentations provably iso- lates content from style. Advances in neural information processing systems34, 16451–16467 (2021)

    2021

  48. [48]

    Wan: Open and Advanced Large-Scale Video Generative Models

    Wan, T., Wang, A., Ai, B., Wen, B., Mao, C., Xie, C.W., Chen, D., Yu, F., Zhao, H., Yang, J., Zeng, J., Wang, J., Zhang, J., Zhou, J., Wang, J., Chen, J., Zhu, K., Zhao, K., Yan, K., Huang, L., Feng, M., Zhang, N., Li, P., Wu, P., Chu, R., Feng, R., Zhang, S., Sun, S., Fang, T., Wang, T., Gui, T., Weng, T., Shen, T., Lin, W., Wang, W., Wang, W., Zhou, W.,...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  49. [49]

    In: Proceedings of the 2024 Con- ference on Empirical Methods in Natural Language Processing

    Wang, J., Wang, C., Huang, K., Huang, J., Jin, L.: Videoclip-xl: Advancing long description understanding for video clip models. In: Proceedings of the 2024 Con- ference on Empirical Methods in Natural Language Processing. pp. 16061–16075 (2024).https://doi.org/10.18653/v1/2024.emnlp-main.898

    work page doi:10.18653/v1/2024.emnlp-main.898 2024

  50. [50]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Wang, J., Ge, Y., Cai, G., Yan, R., Lin, X., Shan, Y., Qie, X., Shou, M.Z.: Object- aware video-language pre-training for retrieval. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 3313–3322 (2022)

    2022

  51. [51]

    arXiv preprint arXiv:2203.07111 (2022)

    Wang, Q., Zhang, Y., Zheng, Y., Pan, P., Hua, X.S.: Disentangled representation learning for text-video retrieval. arXiv preprint arXiv:2203.07111 (2022)

    work page arXiv 2022

  52. [52]

    Advances in Neural Information Processing Systems37, 65618–65642 (2024).https://doi.org/10.52202/079017-2096

    Wang, W., Yang, Y.: Vidprom: A million-scale real prompt-gallery dataset for text- to-video diffusion models. Advances in Neural Information Processing Systems37, 65618–65642 (2024).https://doi.org/10.52202/079017-2096

    work page doi:10.52202/079017-2096 2024

  53. [53]

    arXiv preprint arXiv:2503.01739 (2025) 4

    Wang, W., Yang, Y.: Videoufo: A million-scale user-focused dataset for text-to- video generation. arXiv preprint arXiv:2503.01739 (2025)

    work page arXiv 2025

  54. [54]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Wang, X., Zhu, L., Yang, Y.: T2vlad: Global-local sequence alignment for text- video retrieval. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5079–5088 (2021)

    2021

  55. [55]

    IEEE Transactions on Multimedia25, 6079–6089 (2023).https://doi.org/10.1109/TMM.2022.3204444

    Wang, X., Zhu, L., Zheng, Z., Xu, M., Yang, Y.: Align and tell: Boosting text-video retrieval with local alignment and fine-grained supervision. IEEE Transactions on Multimedia25, 6079–6089 (2023).https://doi.org/10.1109/TMM.2022.3204444

    work page doi:10.1109/tmm.2022.3204444 2023

  56. [56]

    In: The Twelfth International Conference on Learning Representations (2024)

    Wang, Y., He, Y., Li, Y., Li, K., Yu, J., Ma, X., Li, X., Chen, G., Chen, X., Wang, Y., Luo, P., Liu, Z., Wang, Y., Wang, L., Qiao, Y.: Internvid: A large-scale video-text dataset for multimodal understanding and generation. In: The Twelfth International Conference on Learning Representations (2024)

    2024

  57. [57]

    In: European Conference on Computer Vision

    Wang, Y., Li, K., Li, X., Yu, J., He, Y., Chen, G., Pei, B., Zheng, R., Wang, Z., Shi, Y., et al.: Internvideo2: Scaling foundation models for multimodal video un- derstanding. In: European Conference on Computer Vision. pp. 396–416. Springer (2024)

    2024

  58. [58]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Wang, Z., Sung, Y.L., Cheng, F., Bertasius, G., Bansal, M.: Unified coarse-to-fine alignment for video-text retrieval. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 2816–2827 (2023)

    2023

  59. [59]

    In: Proceedings of the 29th ACM international conference on Multimedia

    Wu, P., He, X., Tang, M., Lv, Y., Liu, J.: Hanet: Hierarchical alignment networks for video-text retrieval. In: Proceedings of the 29th ACM international conference on Multimedia. pp. 3518–3527 (2021)

    2021

  60. [60]

    In: Forty-second International Conference on Machine Learning (2025)

    Xie, S., Kong, L., Zheng, Y., Tang, Z., Xing, E.P., Chen, G., Zhang, K.: Learning vision and language concepts for controllable image generation. In: Forty-second International Conference on Machine Learning (2025)

    2025

  61. [61]

    com- press highlights, lift midtones

    Xie, S., Kong, L., Zheng, Y., Yao, Y., Tang, Z., Xing, E.P., Chen, G., Zhang, K.: Smartclip: Modular vision-language alignment with identification guarantees. In: ProceedingsoftheIEEE/CVFConferenceonComputerVisionandPatternRecog- nition. pp. 29780–29790 (2025).https://doi.org/10.1109/CVPR52734.2025. 02772

    work page doi:10.1109/cvpr52734.2025 2025

  62. [62]

    Advances in Neural Information Processing Systems37, 16623–16644 (2024)

    Xiong, T., Wang, Y., Zhou, D., Lin, Z., Feng, J., Liu, X.: Lvd-2m: A long-take video dataset with temporally dense captions. Advances in Neural Information Processing Systems37, 16623–16644 (2024)

    2024

  63. [63]

    arXiv preprint arXiv:2403.08335 (2024) 20 P

    Xu, D., Yao, D., Lachapelle, S., Taslakian, P., Von Kügelgen, J., Locatello, F., Magliacane, S.: A sparsity principle for partially observable causal representation learning. arXiv preprint arXiv:2403.08335 (2024) 20 P. Zhu et al

    work page arXiv 2024

  64. [64]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Xue, H., Hang, T., Zeng, Y., Sun, Y., Liu, B., Yang, H., Fu, J., Guo, B.: Advancing high-resolution video-language representation with large-scale video transcriptions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 16456–16466 (2022)

    2022

  65. [65]

    In: The Eleventh International Conference on Learning Representations (2023)

    Xue, H., Sun, Y., Liu, B., Fu, J., Song, R., Li, H., Luo, J.: Clip-vip: Adapting pre-trained image-text model to video-language representation alignment. In: The Eleventh International Conference on Learning Representations (2023)

    2023

  66. [66]

    arXiv preprint arXiv:2506.13691 , year=

    Xue, Z., Zhang, J., Hu, T., He, H., Chen, Y., Cai, Y., Wang, Y., Wang, C., Liu, Y., Li, X., et al.: Ultravideo: High-quality uhd video dataset with comprehensive captions. arXiv preprint arXiv:2506.13691 (2025)

    work page arXiv 2025

  67. [67]

    In: Proceedings of the AAAI Conference on Artificial Intelligence

    Yang, X., Zhu, L., Wang, X., Yang, Y.: Dgl: Dynamic global-local prompt tun- ing for text-video retrieval. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 6540–6548 (2024)

    2024

  68. [68]

    arXiv preprint arXiv:2311.04056 (2023)

    Yao, D., Xu, D., Lachapelle, S., Magliacane, S., Taslakian, P., Martius, G., von Kügelgen, J., Locatello, F.: Multi-view causal representation learning with partial observability. arXiv preprint arXiv:2311.04056 (2023)

    work page arXiv 2023

  69. [69]

    arXiv preprint arXiv:2202.04828 (2022)

    Yao, W., Chen, G., Zhang, K.: Learning latent causal dynamics. arXiv preprint arXiv:2202.04828 (2022)

    work page arXiv 2022

  70. [70]

    arXiv preprint arXiv:2110.05428 (2021)

    Yao, W., Sun, Y., Ho, A., Sun, C., Zhang, K.: Learning temporally causal latent processes from general temporal data. arXiv preprint arXiv:2110.05428 (2021)

    work page arXiv 2021

  71. [71]

    Yuksekgonul, M., Bianchi, F., Kalluri, P., Jurafsky, D., Zou, J.: When and why vision-language models behave like bags-of-words, and what to do about it? In: The Eleventh International Conference on Learning Representations (2023)

    2023

  72. [72]

    In: European conference on computer vision

    Zhang, B., Zhang, P., Dong, X., Zang, Y., Wang, J.: Long-clip: Unlocking the long- text capability of clip. In: European conference on computer vision. pp. 310–325. Springer (2024)

    2024

  73. [73]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Zhang, W., Wan, C., Liu, T., Tian, X., Shen, X., Ye, J.: Enhanced motion-text alignment for image-to-video transfer learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18504–18515 (2024)

    2024

  74. [74]

    Advances in neural information processing systems35, 16411–16422 (2022)

    Zheng,Y.,Ng,I.,Zhang,K.:Ontheidentifiabilityofnonlinearica:Sparsityandbe- yond. Advances in neural information processing systems35, 16411–16422 (2022)

    2022

  75. [75]

    Zheng, Y., Zhang, K.: Generalizing nonlinear ica beyond structural sparsity. Ad- vances in Neural Information Processing Systems36, 13326–13355 (2023) MoVA: Modular Long Video–Text Alignment 21 Supplementary Material A Proof Our identification analysis shows that the selective, mask-based alignment ob- jective in (3) recovers caption-conditioned semantic ...

    2023

  76. [76]

    Equivalently, PT ˆzT is independent ofϵT

    (Nuisance invariance on the text active block) For every coordinateiwith [PT ]ii = 1and every text-nuisance coordinatej, ∂[ˆzT]i ∂[ϵT]j = 0 . Equivalently, PT ˆzT is independent ofϵT. 22 P. Zhu et al

  77. [77]

    ↑” denotes that higher is better. “↓

    (Blockwise invertible reparameterizations) There exist smooth, locally invert- ible mapsh T andh V (defined on the respective active blocks) such that PT ˆzT =P T hT(zT), P V ˆzV t =P V hV(zV t ). Proof.Step A: Fix projectors and use alignment.Because mask supports are locally constant,PT andP V are fixed binary projectors in the neighborhood. By optimali...