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REVIEW 5 major objections 7 minor 35 references

MamFusion: Multi-Mamba with Temporal Fusion for Partially Relevant Video Retrieval

T0 review · 5 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read MamFusion claims state-of-the-art partially relevant video retrieval by combining a Multi-Mamba module with bidirectional temporal text-video fusion.

desk verdict Plausible incremental PRVR extension whose reported SOTA numbers rest on an unstated label-construction protocol; worth a careful review but needs major revisions. read the letter →

arxiv 2506.03473 v1 pith:LHPSKQM6 submitted 2025-06-04 cs.CV

Xinru Ying , Jiaqi Mo , Jingyang Lin , Canghong Jin , Fangfang Wang , Lina Wei This is my paper
classification cs.CV
keywords partiallyrelevantvideoretrievalMambastatespacemodeltemporalcross-modalfusionvideo-textlonguntrimmedGMMFormerMam
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 tries to show that partially relevant video retrieval—finding an untrimmed video in which only some moments match a text query—gets better when a long-sequence state-space model called Mamba is combined with two temporal cross-modal fusion modules. If the claim is right, MamFusion would be the top-ranked retrieval model in the paper's comparison on ActivityNet Captions, Charades-STA, and TVR, the standard benchmarks for this task. The practical motivation is that real video collections are long and mostly irrelevant to any given query, so retrieval models must identify the few relevant moments without expensive moment-by-moment search. The paper reports higher recall at every rank on Charades-STA and higher SumR on all three datasets than the GMMFormer baseline it extends.

What carries the argument

The central object is the Multi-Mamba module: a selective state-space sequence model (Mamba) inserted after GMMFormer blocks in both the clip-level and video-level branches, which the paper says lets the network dynamically adjust to relevant moments while ignoring irrelevant frames. Around it sit two cross-attention fusion functions: Temporal V-to-T Fusion, where text tokens query the Mamba-processed video features, and Temporal T-to-V Fusion, where video features query the text representation. These are named in the paper; the machinery's job is to replace a purely global similarity score with temporal, query-aware alignment between text and video.

What would settle it

Re-run the released code with a fully specified training loss (for example, InfoNCE over video-query pairs) and an explicitly defined negative-sampling rule that does not peek at moment annotations, then compare MamFusion with GMMFormer under identical conditions; if the SumR gaps on ActivityNet Captions, Charades-STA, or TVR disappear or reverse, the central SOTA claim is settled negative.

Watch

Extended reading notes

Core claim

The paper's central claim is that adding a Multi-Mamba module and two temporal fusion modules—Temporal V-to-T Fusion and Temporal T-to-V Fusion—on top of the GMMFormer clip and video branches produces a PRVR model that outperforms prior T2VR, VCMR, and PRVR methods. The Multi-Mamba module uses selective state-space processing to focus on query-relevant video segments and reduce redundancy in long untrimmed sequences; the two fusion modules use cross-attention in both directions so that the text representation is updated by video content and the video representation is updated by text content. On ActivityNet Captions, Charades-STA, and TVR, the reported SumR values are 147.4, 76.5, and 177.5, the highest in the comparison table. The paper further claims faster and smoother training loss convergence than GMMFormer, and ablation results indicate each module contributes to the final score.

Load-bearing premise

The paper never states the training loss or the exact rule for turning moment-annotated videos into positive and negative video-query pairs, so the reported scores are only as trustworthy as that unstated protocol.

Editorial extensions

If this is right

  • If the reported numbers hold, MamFusion is the strongest published PRVR model on ActivityNet Captions, Charades-STA, and TVR by SumR.
  • Selective state-space models can serve as a drop-in replacement for the heavy clip-embedding machinery in PRVR, since the Multi-Mamba module compresses long sequences while preserving relevant moments.
  • The two temporal fusion directions provide a template for query-aware video scoring that other video-text retrieval systems could reuse without moment supervision.
  • The convergence-speed comparison implies the model reaches usable retrieval quality in fewer training epochs than the GMMFormer baseline, reducing training cost.

Reading between the lines

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

  • The paper leaves the training loss and the construction of positive and negative video-query pairs unspecified, so a natural next step is to test whether the reported margins persist under an explicit contrastive loss with exactly specified negative sampling.
  • Because the moment annotations are deliberately unused, one testable extension is whether higher retrieval rank correlates with the annotated moment's presence at the top of the video—if the model finds videos with relevant moments but ranks them poorly in time, a localization-aware re-ranking could push recall further.
  • If replicated, the results suggest that long-video retrieval can move from quadratic transformer attention to linear-time state-space scanning, which would matter for hour-long real-world video collections.
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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

5 major / 7 minor

Summary. The paper proposes MamFusion, a partially relevant video retrieval (PRVR) model built on top of GMMFormer. It introduces a Multi-Mamba module intended to handle long untrimmed video sequences, along with two temporal fusion modules (Temporal V-to-T Fusion and Temporal T-to-V Fusion) that cross-attend between text and video representations. The model is evaluated on ActivityNet Captions, Charades-STA, and TVR, and the abstract and conclusion claim state-of-the-art retrieval accuracy. The method section describes sentence and video feature extraction, GMMFormer blocks, Mamba processing, and the two fusion modules, but it does not specify a training loss or the exact protocol for constructing video-level relevance labels. The experiments report R@K and SumR metrics, with an ablation study on Charades-STA. The paper also includes a convergence speed comparison and a code link.

Significance. If the claims were fully substantiated, the paper would show that a selective state-space model (Mamba) can improve PRVR retrieval over the transformer-based GMMFormer, and that bidirectional temporal fusion between video and text is beneficial. The paper makes a concrete architectural contribution and provides a public code link, which are positive elements. However, the central state-of-the-art claim is currently unverifiable because the label construction process is not specified, the training loss is omitted, the reported R@1 numbers do not consistently favor MamFusion over GMMFormer, and the ablation table contradicts the narrative for the V-to-T module. The paper also omits recent PRVR baselines (DL-DKD and PEAN) that it cites in the introduction. The potential significance is real, but the present manuscript does not yet establish it rigorously.

major comments (5)
  1. [Section III-A.1] The statement that 'moment annotations provided by these datasets were not used in our experiments' makes the ground truth for the reported R@K/SumR numbers undefined. In the standard PRVR protocol, video-level relevance labels are derived from moment annotations. Please specify exactly how positive and negative video-query pairs are constructed for each dataset; if moment annotations are used only for labeling and not at inference, state that explicitly and provide the pair-generation procedure. Without this, all results in Table I are unverifiable.
  2. [Section II] No training loss is specified anywhere in the method section. The paper describes the model architecture and gives equations (1)-(5), but never states the objective used for optimization (e.g., a contrastive loss over cosine similarities, with what negative sampling strategy). Add the loss formulation and training details, since the reported retrieval performance depends on the training objective.
  3. [Section III-B, Table I, abstract] The abstract and conclusion claim state-of-the-art retrieval accuracy, but on the metric declared primary in Section III-A.2 (R@K, K=1,5,10,100), MamFusion is worse than GMMFormer on R@1 on ActivityNet Captions (8.0 vs 8.3) and Charades-STA (2.0 vs 2.1). The claim is only defensible if SumR is the designated primary metric; please state this explicitly and justify it, or adjust the claim.
  4. [Section III-C, Table II] Table II shows that removing Temporal V-to-T Fusion ('w/o V2T') yields R@1=2.3, which is higher than the full model's R@1=2.0. The text states that removing either fusion module 'resulted in a significant drop in retrieval performance,' which is contradicted by this row. Please reconcile this discrepancy or provide a corrected ablation table and a revised interpretation of V2T's contribution.
  5. [Section III-B, Table I] The comparison set omits DL-DKD [18] and PEAN [19], both of which are introduced in Section I as existing PRVR methods. A state-of-the-art claim against 'existing methods' requires these recent baselines. Also report the variance or standard deviation across multiple runs for the key numbers, since the differences between models are small (e.g., SumR 177.5 vs 176.6 on TVR).
minor comments (7)
  1. [Section II-C] The subsection titles and body text are swapped: Section II-C.1 is titled 'Temporal V-to-T Fusion' but describes the module as 'Temporal T-to-V Fusion,' and Section II-C.2 has the reverse error; equations (3) and (4) use TVT and TTV respectively. Please align the names.
  2. [Section III-A.2] Table II uses R@2 instead of R@5, which is inconsistent with the metric definition given in this section; please standardize.
  3. [Section III-D] The heading 'Inference Convergence Speed' describes training loss convergence; please rename the section (e.g., 'Training Convergence').
  4. [Section III-A.3] The implementation details omit the optimizer, learning rate, batch size, number of epochs, and hardware; include these for reproducibility.
  5. [Section II-B] The clip-level branch is said to use 'two GMMFormer blocks,' while the implementation in Section III-A.3 lists four Gaussian variances (0.5, 1.0, 5.0, ∞); clarify the number of GMMFormer blocks and how the variances map to blocks.
  6. [Figure 3] The heatmaps lack labeled axes and a color scale; the caption states 'Darker regions indicate higher relevance' but the color mapping is not defined, making the visualization difficult to interpret.
  7. [Section II-D] The term 'Multi-Mamba' is not explained: Eq. (5) uses a single Mamba function, and the text says the module is integrated into both branches. Please define what makes it 'multi' (e.g., multiple Mamba blocks in parallel or in sequence) and describe the Mamba architecture (e.g., selective state space parameters) beyond the hyperparameters listed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found; the paper's claims rest on external benchmarks and standard architectural components.

full rationale

The paper makes no derivation that reduces a prediction to an input by construction. MamFusion's modules, specified in Equations 1-5, are standard attention, fusion, and state-space operations; no parameter is fitted to a target retrieval metric and then renamed as a prediction. The state-of-the-art claim rests on Table I comparisons against published baselines on three external datasets and on the ablations in Table II, which are external, falsifiable evaluations rather than consequences of the model's definition. The citation of GMMFormer [17] is a routine architectural reuse with no author overlap and no uniqueness or ansatz-forcing role; the temporal fusion modules are presented with their own formulas and are not defined in terms of the claimed outcome. The absence of an explicit loss function and of the exact positive/negative pair construction from moment annotations is a reproducibility and verifiability concern, not a circularity concern, because nothing in the paper defines the evaluation metric in terms of the model's own outputs. No self-definitional, fitted-input-called-prediction, self-citation-load-bearing, or renaming-known-result step is present.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities; the proposed modules are architectural components. Free parameters are hyperparameters tuned by hand. Axioms are standard domain assumptions about pretrained features, evaluation protocols, and the Mamba module's capabilities, none of which are verified within the paper.

free parameters (4)
  • Mamba state expansion factor dstate = 16
    Selected by hand or tuned for performance, described in Section III-A.3; affects the Mamba module's representational capacity.
  • Mamba local convolution width dconv = 4
    Selected by hand or tuned, described in Section III-A.3; affects temporal locality in the Mamba block.
  • Mamba block expansion factor expand = 2
    Selected by hand or tuned, described in Section III-A.3; affects the hidden state width.
  • Gaussian variances for GMMFormer blocks = 0.5, 1.0, 5.0, infinity
    Inherited from GMMFormer [17] but still set manually; affects multi-scale temporal aggregation.
assumptions (5)
  • domain assumption Pre-trained RoBERTa features provide adequate text representations.
    Section II-A uses 1024- or 768-dimensional RoBERTa features from prior work without re-validation.
  • domain assumption Pre-extracted I3D and ResNet152 features are reliable video inputs.
    Section III-A.3 relies on features provided by [16], [29], and [28] without verifying their quality for PRVR.
  • domain assumption The PRVR evaluation protocol from prior work applies to ActivityNet, Charades-STA, and TVR.
    Section III-A.1 mentions ignoring moment annotations but does not specify how video-level relevance labels are derived.
  • domain assumption A single Mamba block after GMMFormer captures long-range temporal dependencies.
    Section II-D states this without empirical verification of the Mamba block's necessity for long sequences.
  • domain assumption Cosine similarity is the appropriate retrieval scoring function.
    Figure 2 caption mentions cosine similarity for retrieval, but no training objective or margin is defined.

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Cite this review

Pith. "Pith review of MamFusion: Multi-Mamba with Temporal Fusion for Partially Relevant Video Retrieval." pith.science (2026). https://pith.science/paper/LHPSKQM6

@misc{pith2026250603473,
  author       = {Pith},
  title        = {Pith review of: MamFusion: Multi-Mamba with Temporal Fusion for Partially Relevant Video Retrieval},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LHPSKQM6}},
  note         = {Machine review of arXiv:2506.03473}
}
read the original abstract

Partially Relevant Video Retrieval (PRVR) is a challenging task in the domain of multimedia retrieval. It is designed to identify and retrieve untrimmed videos that are partially relevant to the provided query. In this work, we investigate long-sequence video content understanding to address information redundancy issues. Leveraging the outstanding long-term state space modeling capability and linear scalability of the Mamba module, we introduce a multi-Mamba module with temporal fusion framework (MamFusion) tailored for PRVR task. This framework effectively captures the state-relatedness in long-term video content and seamlessly integrates it into text-video relevance understanding, thereby enhancing the retrieval process. Specifically, we introduce Temporal T-to-V Fusion and Temporal V-to-T Fusion to explicitly model temporal relationships between text queries and video moments, improving contextual awareness and retrieval accuracy. Extensive experiments conducted on large-scale datasets demonstrate that MamFusion achieves state-of-the-art performance in retrieval effectiveness. Code is available at the link: https://github.com/Vision-Multimodal-Lab-HZCU/MamFusion.

Figures

Figures reproduced from arXiv: 2506.03473 by the authors.

Figure 1
Figure 1. An illustrative of conventional Text-to-video Retrieval and Partially Relevant Video Retrieval: (a) The T2VR retrieves video files from a trimmed video collection. (b) The PRVR retrieves video files from an unmodified long video collection and the retrieved video is partially associate with a text query. MSVD [11], MSR-VTT [12], and VATEX [13]. These datasets typically assume short, trimmed videos with a direct alig… view at source ↗
Figure 2
Figure 2. An overview of our proposed MamFusion framework. Text features are extracted using RoBERTa, while video features are extracted using a 2D/3D CNN. The video representation is processed through clip-level and video-level branches. Temporal V-to-T Fusion and Temporal T-to-V Fusion enhance text-video interaction by capturing temporal context. The Mamba module dynamically focuses on relevant video moments, and cosine sim… view at source ↗
Figure 3
Figure 3. Attention Heatmaps for Video-to-Text and Text-to-Video Mappings. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: A comparison of the average loss convergence during the training [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

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Works this paper leans on

35 extracted references · 31 canonical work pages

  1. [18]

    Dual learning with dynamic knowledge distillation for partially relevant video retrieval,

    Jianfeng Dong, Minsong Zhang, Zheng Zhang, Xianke Chen, Daizong Liu, Xiaoye Qu, Xun Wang, and Baolong Liu, “Dual learning with dynamic knowledge distillation for partially relevant video retrieval,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 11302–11312

    work page 2023

  2. [19]

    Progressive event alignment network for partial relevant video retrieval,

    Xun Jiang, Zhiguo Chen, Xing Xu, Fumin Shen, Zuo Cao, and Xunliang Cai, “Progressive event alignment network for partial relevant video retrieval,” in 2023 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2023, pp. 1973–1978

    work page 2023

  3. [1]

    Frozen in time: A joint video and image encoder for end-to-end retrieval,

    Max Bain, Arsha Nagrani, G ¨ul Varol, and Andrew Zisserman, “Frozen in time: A joint video and image encoder for end-to-end retrieval,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 1728–1738

    work page 2021

  4. [2]

    Fine-grained video- text retrieval with hierarchical graph reasoning,

    Shizhe Chen, Yida Zhao, Qin Jin, and Qi Wu, “Fine-grained video- text retrieval with hierarchical graph reasoning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 10638–10647

    work page 2020

  5. [3]

    Dual encoding for video retrieval by text,

    Jianfeng Dong, Xirong Li, Chaoxi Xu, Xun Yang, Gang Yang, Xun Wang, and Meng Wang, “Dual encoding for video retrieval by text,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 8, pp. 4065–4080, 2021

    work page 2021

  6. [4]

    Hierarchical cross-modal graph consistency learn- ing for video-text retrieval,

    Weike Jin, Zhou Zhao, Pengcheng Zhang, Jieming Zhu, Xiuqiang He, and Yueting Zhuang, “Hierarchical cross-modal graph consistency learn- ing for video-text retrieval,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021, pp. 1114–1124

    work page 2021

  7. [5]

    De- confounded video moment retrieval with causal intervention,

    Xun Yang, Fuli Feng, Wei Ji, Meng Wang, and Tat-Seng Chua, “De- confounded video moment retrieval with causal intervention,” in Pro- ceedings of the 44th international ACM SIGIR conference on research and development in information retrieval , 2021, pp. 1–10

    work page 2021

  8. [6]

    Cross-lingual cross-modal retrieval with noise- robust learning,

    Yabing Wang, Jianfeng Dong, Tianxiang Liang, Minsong Zhang, Rui Cai, and Xun Wang, “Cross-lingual cross-modal retrieval with noise- robust learning,” in Proceedings of the 30th ACM International Confer- ence on Multimedia , 2022, pp. 422–433

    work page 2022

Show all 35 references
  1. [7]

    Partially relevant video retrieval,

    Jianfeng Dong, Xianke Chen, Minsong Zhang, Xun Yang, Shujie Chen, Xirong Li, and Xun Wang, “Partially relevant video retrieval,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, pp. 246–257

    2022

  2. [8]

    Bridging video-text retrieval with multiple choice questions,

    Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, and Ping Luo, “Bridging video-text retrieval with multiple choice questions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16167–16176

    2022

  3. [9]

    T2vlad: global-local sequence alignment for text-video retrieval,

    Xiaohan Wang, Linchao Zhu, and Yi Yang, “T2vlad: global-local sequence alignment for text-video retrieval,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 5079–5088

    2021

  4. [10]

    Prota: Probabilistic token aggregation for text-video retrieval,

    Han Fang, Xianghao Zang, Chao Ban, Zerun Feng, Lanxiang Zhou, Zhongjiang He, Yongxiang Li, and Hao Sun, “Prota: Probabilistic token aggregation for text-video retrieval,” arXiv preprint arXiv:2404.12216 , 2024

    2024 arXiv

  5. [11]

    Collecting highly parallel data for paraphrase evaluation,

    David Chen and William B Dolan, “Collecting highly parallel data for paraphrase evaluation,” in Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, 2011, pp. 190–200

    2011

  6. [12]

    Msr-vtt: A large video description dataset for bridging video and language,

    Jun Xu, Tao Mei, Ting Yao, and Yong Rui, “Msr-vtt: A large video description dataset for bridging video and language,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 5288–5296

    2016

  7. [13]

    Vatex: A large-scale, high-quality multilin- gual dataset for video-and-language research,

    Xin Wang, Jiawei Wu, Junkun Chen, Lei Li, Yuan-Fang Wang, and William Yang Wang, “Vatex: A large-scale, high-quality multilin- gual dataset for video-and-language research,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 4581– 4591

    2019

  8. [14]

    Text-based localization of moments in a video corpus,

    Sudipta Paul, Niluthpol Chowdhury Mithun, and Amit K Roy- Chowdhury, “Text-based localization of moments in a video corpus,” IEEE Transactions on Image Processing, vol. 30, pp. 8886–8899, 2021

    2021

  9. [15]

    Siamese alignment network for weakly supervised video moment retrieval,

    Yunxiao Wang, Meng Liu, Yinwei Wei, Zhiyong Cheng, Yinglong Wang, and Liqiang Nie, “Siamese alignment network for weakly supervised video moment retrieval,” IEEE Transactions on Multimedia, vol. 25, pp. 3921–3933, 2022

    2022

  10. [16]

    A hierarchical multi-modal encoder for moment localization in video corpus,

    Bowen Zhang, Hexiang Hu, Joonseok Lee, Ming Zhao, Sheide Cham- mas, Vihan Jain, Eugene Ie, and Fei Sha, “A hierarchical multi-modal encoder for moment localization in video corpus,” arXiv preprint arXiv:2011.09046, 2020

    2011 arXiv

  11. [17]

    Gmmformer: Gaussian-mixture-model based transformer for efficient partially relevant video retrieval,

    Yuting Wang, Jinpeng Wang, Bin Chen, Ziyun Zeng, and Shu-Tao Xia, “Gmmformer: Gaussian-mixture-model based transformer for efficient partially relevant video retrieval,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2024, vol. 38, pp. 5767–5775

    2024

  12. [20]

    Catr: Combinatorial-dependence audio-queried transformer for audio-visual video segmentation,

    Kexin Li, Zongxin Yang, Lei Chen, Yi Yang, and Jun Xiao, “Catr: Combinatorial-dependence audio-queried transformer for audio-visual video segmentation,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023, pp. 1485–1494

    2023

  13. [21]

    Roberta: A robustly optimized bert pretraining approach,

    Yinhan Liu, “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692 , vol. 364, 2019

    1907 arXiv

  14. [22]

    Deep residual learning for image recognition,

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770– 778

    2016

  15. [23]

    Layer normalization,

    Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton, “Layer normalization,” ArXiv e-prints, pp. arXiv–1607, 2016

    2016

  16. [24]

    Multi-modal fusion and query refinement network for video moment retrieval and highlight detection,

    Yifang Xu, Yunzhuo Sun, Benxiang Zhai, Zien Xie, Youyao Jia, and Sidan Du, “Multi-modal fusion and query refinement network for video moment retrieval and highlight detection,” in 2024 IEEE International Conference on Multimedia and Expo (ICME) . IEEE, 2024, pp. 1–6

    2024

  17. [25]

    Attention is all you need,

    A Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems, 2017

    2017

  18. [26]

    Dense-captioning events in videos,

    Ranjay Krishna, Kenji Hata, Frederic Ren, Li Fei-Fei, and Juan Car- los Niebles, “Dense-captioning events in videos,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 706–715

    2017

  19. [27]

    Tall: Temporal activity localization via language query,

    Jiyang Gao, Chen Sun, Zhenheng Yang, and Ram Nevatia, “Tall: Temporal activity localization via language query,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 5267– 5275

    2017

  20. [28]

    Tvr: A large- scale dataset for video-subtitle moment retrieval,

    Jie Lei, Licheng Yu, Tamara L Berg, and Mohit Bansal, “Tvr: A large- scale dataset for video-subtitle moment retrieval,” in Computer Vision– ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI 16 . Springer, 2020, pp. 447–463

    2020

  21. [29]

    Local-global video-text interactions for temporal grounding,

    Jonghwan Mun, Minsu Cho, and Bohyung Han, “Local-global video-text interactions for temporal grounding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10810–10819

    2020

  22. [30]

    Quo vadis, action recognition? a new model and the kinetics dataset,

    Joao Carreira and Andrew Zisserman, “Quo vadis, action recognition? a new model and the kinetics dataset,” in proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 6299–6308

    2017

  23. [31]

    Reading-strategy inspired visual representation learning for text-to-video retrieval,

    Jianfeng Dong, Yabing Wang, Xianke Chen, Xiaoye Qu, Xirong Li, Yuan He, and Xun Wang, “Reading-strategy inspired visual representation learning for text-to-video retrieval,” IEEE transactions on circuits and systems for video technology , vol. 32, no. 8, pp. 5680–5694, 2022

    2022

  24. [32]

    Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning,

    Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, and Tianrui Li, “Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning,” Neurocomputing, vol. 508, pp. 293–304, 2022

    2022

  25. [33]

    Cap4video: What can auxiliary captions do for text-video retrieval?,

    Wenhao Wu, Haipeng Luo, Bo Fang, Jingdong Wang, and Wanli Ouyang, “Cap4video: What can auxiliary captions do for text-video retrieval?,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 10704–10713

    2023

  26. [34]

    Video corpus mo- ment retrieval with contrastive learning,

    Hao Zhang, Aixin Sun, Wei Jing, Guoshun Nan, Liangli Zhen, Joey Tianyi Zhou, and Rick Siow Mong Goh, “Video corpus mo- ment retrieval with contrastive learning,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval,...

    2021

  27. [35]

    Conquer: Contextual query-aware ranking for video corpus moment retrieval,

    Zhijian Hou, Chong-Wah Ngo, and Wing Kwong Chan, “Conquer: Contextual query-aware ranking for video corpus moment retrieval,” in Proceedings of the 29th ACM International Conference on Multimedia , 2021, pp. 3900–3908

    2021

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