pith. sign in

arxiv: 2601.14594 · v2 · submitted 2026-01-21 · 💻 cs.CV

LFS: Learnable Frame Selector for Event-Aware and Temporally Diverse Video Captioning

Lianying Chao , Linfeng Yin , Peiyu Ren , Yifan Jiang , Qiaoyu Ren , Dingcheng Shan , Jing-cheng Pang , Sijie Wu
show 3 more authors
Xubin Li Kai Zhang Xin Chen
This is my paper

Pith reviewed 2026-05-16 12:17 UTC · model grok-4.3

classification 💻 cs.CV
keywords video captioningframe selectionlearnable selectortemporal diversityevent relevancevideo LLMbenchmark evaluation
0
0 comments X

The pith

A learnable frame selector improves detailed video captioning by choosing temporally diverse, event-relevant frames.

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

The paper introduces a Learnable Frame Selector (LFS) to replace uniform frame sampling in video captioning models that use LLMs. Uniform sampling ignores how events are unevenly distributed in time, so LFS learns to pick frames that are both spread out and focused on important events. It does this by training on feedback from the captions generated by a frozen video-LLM, directly optimizing for better final descriptions. The authors also create a new benchmark ICH-CC with questions designed to match human understanding of videos. Experiments show consistent gains, with larger improvements on the new benchmark and benefits for downstream video question answering.

Core claim

LFS models temporal importance to balance diversity and relevance, uses a stratified strategy for coverage, and leverages caption feedback from frozen video-LLMs to learn selections that optimize downstream caption quality, leading to improved performance on video captioning benchmarks including a new human-consistent one called ICH-CC.

What carries the argument

The Learnable Frame Selector (LFS), which explicitly models temporal importance with a stratified sampling strategy and trains using caption feedback to select event-aware and temporally diverse frames.

Load-bearing premise

Feedback from captions produced by a frozen video-LLM provides a reliable and unbiased training signal for learning optimal frame selection without the selector overfitting to idiosyncrasies of that particular LLM.

What would settle it

Training LFS on one video-LLM and testing whether the selected frames produce higher-quality captions than uniform sampling when used with a different video-LLM or on unseen video datasets would falsify the reliability of the feedback signal.

Figures

Figures reproduced from arXiv: 2601.14594 by Dingcheng Shan, Jing-cheng Pang, Kai Zhang, Lianying Chao, Linfeng Yin, Peiyu Ren, Qiaoyu Ren, Sijie Wu, Xin Chen, Xubin Li, Yifan Jiang.

Figure 1
Figure 1. Figure 1: Performance comparison between baselines and the LFS [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The overall scheme of the proposed learnable frame selector (LFS). (a) and (b) are the training and inference process of LFS, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The architecture of temporal scoring network (TSNet). [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overview of ICH-CC construction pipeline. The five [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results on the ICH-CC-en benchmark. The left shows Qwen3-VL-8B using uniform sampling and our LFS. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
read the original abstract

Video captioning models convert frames into visual tokens and generate descriptions with large language models (LLMs). Since encoding all frames is prohibitively expensive, uniform sampling is the default choice, but it enforces equal temporal coverage while ignoring the uneven events distribution. This motivates a Learnable Frame Selector (LFS) that selects temporally diverse and event-relevant frames. LFS explicitly models temporal importance to balance temporal diversity and event relevance, and employs a stratified strategy to ensure temporal coverage while avoiding clustering. Crucially, LFS leverages caption feedback from frozen video-LLMs to learn frame selection that directly optimizes downstream caption quality. Additionally, we identify the gap between existing benchmark and human's cognition. Thus, we introduce ICH-CC built from carefully designed questions by annotators that reflect human-consistent understanding of video. Experiments indicate that LFS consistently improves detailed video captioning across two representative community benchmarks and ICH-CC, achieving up to 2.0% gains on VDC and over 4% gains on ICH-CC. Moreover, we observe that enhanced captions with LFS leads to improved performance on video question answering. Overall, LFS provides an effective and easy-to-integrate solution for detailed video captioning.

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

3 major / 2 minor

Summary. The paper proposes LFS, a learnable frame selector for video captioning that explicitly models temporal importance, uses stratified sampling to ensure coverage, and trains directly on caption-quality feedback from a frozen video-LLM to optimize downstream captioning. It introduces the ICH-CC benchmark constructed from human-consistent questions and reports consistent gains (up to 2% on VDC, >4% on ICH-CC) plus downstream VQA improvements.

Significance. If the central claim holds, LFS offers a lightweight, integrable module that improves detailed video captioning by replacing uniform sampling with event-aware selection, potentially lowering compute while boosting quality on standard benchmarks and the new ICH-CC; the downstream VQA gains further suggest broader utility for video understanding pipelines.

major comments (3)
  1. [Experiments] Experiments section: reported gains (2.0% VDC, >4% ICH-CC) are presented without error bars, statistical significance tests, or complete ablation tables isolating the contribution of temporal modeling versus stratified sampling versus the LLM feedback objective, undermining assessment of robustness.
  2. [Method] Method section (training objective): LFS is optimized end-to-end using caption scores from the same frozen video-LLM family it will later serve, creating a closed training loop; no cross-LLM transfer experiments are shown to rule out overfitting to model-specific tokenization or hallucination patterns rather than objective event coverage.
  3. [ICH-CC] ICH-CC construction: the paper states the benchmark was built from 'carefully designed questions by annotators that reflect human-consistent understanding' but provides no inter-annotator agreement statistics, validation protocol, or comparison to existing VDC questions, leaving the claimed gap unquantified.
minor comments (2)
  1. [Abstract] Abstract: the percentage gains should explicitly name the underlying metrics (CIDEr, METEOR, etc.) rather than stating '2.0% gains on VDC'.
  2. [Method] Notation: the distinction between 'temporal importance' modeling and the 'stratified strategy' is introduced in the abstract but not clearly separated in the method equations or pseudocode.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important aspects of experimental robustness, training design, and benchmark validation. We address each point below and indicate the changes planned for the revised manuscript.

read point-by-point responses

  1. Referee: Experiments section: reported gains (2.0% VDC, >4% ICH-CC) are presented without error bars, statistical significance tests, or complete ablation tables isolating the contribution of temporal modeling versus stratified sampling versus the LLM feedback objective, undermining assessment of robustness.

    Authors: We agree that the current presentation of results would benefit from greater statistical rigor. In the revised manuscript we will report all main results with error bars computed across multiple random seeds, include paired statistical significance tests for the observed gains, and expand the ablation table to fully isolate the individual contributions of temporal importance modeling, stratified sampling, and the LLM feedback objective. revision: yes

  2. Referee: Method section (training objective): LFS is optimized end-to-end using caption scores from the same frozen video-LLM family it will later serve, creating a closed training loop; no cross-LLM transfer experiments are shown to rule out overfitting to model-specific tokenization or hallucination patterns rather than objective event coverage.

    Authors: The closed-loop design is deliberate: LFS is trained to maximize caption quality for the target video-LLM, which aligns with the downstream use case. We maintain that the objective encourages genuine event coverage rather than model-specific artifacts. To further substantiate this, we will add cross-family transfer experiments in the revision, training LFS on one video-LLM and evaluating the resulting frame selections with a different model family. revision: partial

  3. Referee: ICH-CC construction: the paper states the benchmark was built from 'carefully designed questions by annotators that reflect human-consistent understanding' but provides no inter-annotator agreement statistics, validation protocol, or comparison to existing VDC questions, leaving the claimed gap unquantified.

    Authors: We acknowledge that additional documentation of ICH-CC is warranted. In the revised manuscript we will report inter-annotator agreement statistics, describe the annotation validation protocol in detail, and include a quantitative comparison of question characteristics between ICH-CC and VDC to better quantify the claimed gap in human-consistent coverage. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation or training chain

full rationale

The paper describes LFS as a learnable selector trained via caption feedback from a frozen video-LLM to directly optimize downstream caption quality, with empirical gains reported on VDC (up to 2.0%), ICH-CC (over 4%), and downstream VQA. This constitutes a standard supervised or reinforcement-style training loop rather than a self-definitional or fitted-input reduction: the LLM remains frozen, the selector is a separate module, and evaluation occurs on held-out benchmarks including a newly introduced human-annotated set (ICH-CC). No equations, self-citations, or ansatzes are present in the provided text that would render the claimed improvements equivalent to the inputs by construction. The approach is self-contained against external benchmarks and does not invoke uniqueness theorems or prior author results as load-bearing premises.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so the precise free parameters and implementation details cannot be audited. The central claim rests on the domain assumption that caption feedback supplies a useful learning signal and on the implicit claim that the stratified selector adds coverage without introducing new biases.

free parameters (1)
  • frame selector parameters
    The learnable weights of the temporal importance model are fitted to caption feedback during training.
axioms (1)
  • domain assumption Caption feedback from a frozen video-LLM is a valid proxy for frame selection quality
    Invoked when the selector is trained to maximize downstream caption performance.

pith-pipeline@v0.9.0 · 5548 in / 1334 out tokens · 53312 ms · 2026-05-16T12:17:35.978428+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

33 extracted references · 33 canonical work pages · 2 internal anchors

  1. [1]

    An overview on the evalu- ated video retrieval tasks at trecvid 2022,

    [Awadet al., 2023 ] George Awad, Keith Curtis, Asad Butt, Jonathan Fiscus, Afzal Godil, Yooyoung Lee, Andrew Delgado, Eliot Godard, Lukas Diduch, Jeffrey Liu, Yvette Graham, and Georges Quenot. An overview on the evalu- ated video retrieval tasks at trecvid 2022,

    work page 2023

  2. [2]

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

    [Bainet al., 2021 ] Max Bain, Arsha Nagrani, G¨ul Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. InIEEE Interna- tional Conference on Computer Vision,

    work page 2021

  3. [3]

    Flexible frame selection for efficient video reasoning

    [Buchet al., 2025 ] Shyamal Buch, Arsha Nagrani, Anurag Arnab, and Cordelia Schmid. Flexible frame selection for efficient video reasoning. In2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 29071–29082,

    work page 2025

  4. [4]

    [Chaiet al., 2025 ] Wenhao Chai, Enxin Song, Yilun Du, Chenlin Meng, Vashisht Madhavan, Omer Bar-Tal, Jenq- Neng Hwang, Saining Xie, and Christopher D. Manning. Auroracap: Efficient, performant video detailed caption- ing and a new benchmark,

    work page 2025

  5. [5]

    Longvila: Scaling long-context visual language models for long videos,

    [Chenet al., 2024 ] Yukang Chen, Fuzhao Xue, Dacheng Li, Qinghao Hu, Ligeng Zhu, Xiuyu Li, Yunhao Fang, Hao- tian Tang, Shang Yang, Zhijian Liu, Ethan He, Hongxu Yin, Pavlo Molchanov, Jan Kautz, Linxi Fan, Yuke Zhu, Yao Lu, and Song Han. Longvila: Scaling long-context visual language models for long videos,

    work page 2024

  6. [6]

    M Arzani, and Luc Van Gool

    [Dibaet al., 2023 ] Ali Diba, Vivek Sharma, Mohammad. M Arzani, and Luc Van Gool. Spatio-temporal convolution- attention video network. In2023 IEEE/CVF Interna- tional Conference on Computer Vision Workshops (IC- CVW), pages 859–869,

    work page 2023

  7. [7]

    Implicit location-caption alignment via complementary masking for weakly-supervised dense video captioning,

    [Geet al., 2024 ] Shiping Ge, Qiang Chen, Zhiwei Jiang, Yafeng Yin, Liu Qin, Ziyao Chen, and Qing Gu. Implicit location-caption alignment via complementary masking for weakly-supervised dense video captioning,

    work page 2024

  8. [8]

    Logic-in-frames: Dynamic keyframe search via visual semantic-logical verification for long video un- derstanding

    [Guoet al., 2025 ] Weiyu Guo, Ziyang Chen, Shaoguang Wang, Jianxiang He, Yijie Xu, Jinhui Ye, Ying Sun, and Hui Xiong. Logic-in-frames: Dynamic keyframe search via visual semantic-logical verification for long video un- derstanding. InAdvances in Neural Information Process- ing Systems,

    work page 2025

  9. [9]

    Vlab: Enhancing video language pretraining by feature adapting and blend- ing.IEEE Transactions on Multimedia, 27:2168–2180,

    [Heet al., 2025 ] Xingjian He, Sihan Chen, Fan Ma, Zhicheng Huang, Xiaojie Jin, Zikang Liu, Dongmei Fu, Yi Yang, Jing Liu, and Jiashi Feng. Vlab: Enhancing video language pretraining by feature adapting and blend- ing.IEEE Transactions on Multimedia, 27:2168–2180,

    work page 2025

  10. [10]

    M- llm based video frame selection for efficient video under- standing

    [Huet al., 2025 ] Kai Hu, Feng Gao, Xiaohan Nie, Peng Zhou, Son Tran, Tal Neiman, Lingyun Wang, Mubarak Shah, Raffay Hamid, Bing Yin, and Trishul Chilimbi. M- llm based video frame selection for efficient video under- standing

    work page 2025

  11. [11]

    To- wards efficient visual-language alignment of the q-former for visual reasoning tasks,

    [Kimet al., 2024 ] Sungkyung Kim, Adam Lee, Junyoung Park, Andrew Chung, Jusang Oh, and Jay-Yoon Lee. To- wards efficient visual-language alignment of the q-former for visual reasoning tasks,

    work page 2024

  12. [12]

    Abdelrahman, and Mohamed Abdel-Aty

    [Kimet al., 2025 ] Younggun Kim, Ahmed S. Abdelrahman, and Mohamed Abdel-Aty. Vru-accident: A vision- language benchmark for video question answering and dense captioning for accident scene understanding. InPro- ceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pages 761–771, Oc- tober

    work page 2025

  13. [13]

    Tgif: A new dataset and benchmark on an- imated gif description

    [Liet al., 2016 ] Yuncheng Li, Yale Song, Liangliang Cao, Joel Tetreault, Larry Goldberg, Alejandro Jaimes, and Jiebo Luo. Tgif: A new dataset and benchmark on an- imated gif description. In2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4641–4650,

    work page 2016

  14. [14]

    Videochat: Chat-centric video understanding,

    [Liet al., 2024 ] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. Videochat: Chat-centric video understanding,

    work page 2024

  15. [15]

    Maxinfo: A training-free key-frame selection method us- ing maximum volume for enhanced video understanding

    [Liet al., 2025 ] Pengyi Li, Irina Abdullaeva, Alexander Gambashidze, Andrey Kuznetsov, and Ivan Oseledets. Maxinfo: A training-free key-frame selection method us- ing maximum volume for enhanced video understanding. arXiv preprint arXiv:2502.03183,

    work page arXiv 2025

  16. [16]

    Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

    [Linet al., 2023 ] Bin Lin, Bin Zhu, Yang Ye, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united vi- sual representation by alignment before projection.arXiv preprint arXiv:2311.10122,

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  17. [17]

    Video-chatgpt: Towards detailed video understanding via large vision and language models

    [Maazet al., 2024 ] Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024),

    work page 2024

  18. [18]

    Dense video captioning: A survey of tech- niques, datasets and evaluation protocols.ACM Comput

    [Qasimet al., 2025 ] Iqra Qasim, Alexander Horsch, and Dilip Prasad. Dense video captioning: A survey of tech- niques, datasets and evaluation protocols.ACM Comput. Surv., 57(6), February

    work page 2025

  19. [19]

    Sigurdsson, G ¨ul Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta

    [Sigurdssonet al., 2016 ] Gunnar A. Sigurdsson, G ¨ul Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta. Hollywood in homes: Crowdsourcing data col- lection for activity understanding. In Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling, editors,Computer Vi- sion – ECCV 2016, pages 510–526, Cham,

    work page 2016

  20. [20]

    Springer International Publishing. [Songet al., 2024 ] Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, Yan Lu, Jenq-Neng Hwang, and Gaoang Wang. Moviechat: From dense token to sparse memory for long video understand- ing. In2024 IEEE/CVF Conference on Computer Vision and Pattern...

    work page 2024

  21. [21]

    Tspo: Temporal sampling policy optimization for long- form video language understanding,

    [Tanget al., 2025a ] Canhui Tang, Zifan Han, Hongbo Sun, Sanping Zhou, Xuchong Zhang, Xin Wei, Ye Yuan, Jinglin Xu, and Hao Sun. Tspo: Temporal sampling policy op- timization for long-form video language understanding. arXiv preprint arXiv:2508.04369,

    work page arXiv

  22. [22]

    Adaptive keyframe sampling for long video understanding.arXiv Preprint, 2025

    [Tanget al., 2025b ] Xi Tang, Jihao Qiu, Lingxi Xie, Yunjie Tian, Jianbin Jiao, and Qixiang Ye. Adaptive keyframe sampling for long video understanding.arXiv preprint arXiv:2502.21271,

    work page arXiv

  23. [23]

    Keyframes selection from multiscene videos for stress detection.Information Processing and Management, 62(5):104215,

    [Tianet al., 2025 ] Junrui Tian, Zexi Lin, Yi Dai, Yang Ding, Jinlei Liu, Lei Cao, and Ling Feng. Keyframes selection from multiscene videos for stress detection.Information Processing and Management, 62(5):104215,

    work page 2025

  24. [24]

    Tarsier: Recipes for training and evaluating large video description models,

    [Wanget al., 2024 ] Jiawei Wang, Liping Yuan, Yuchen Zhang, and Haomiao Sun. Tarsier: Recipes for training and evaluating large video description models,

    work page 2024

  25. [25]

    [Wuet al., 2023 ] Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and Philip S. Yu. Multimodal large lan- guage models: A survey. In2023 IEEE International Con- ference on Big Data (BigData), pages 2247–2256,

    work page 2023

  26. [26]

    Event-equalized dense video captioning

    [Wuet al., 2025 ] Kangyi Wu, Pengna Li, Jingwen Fu, Yizhe Li, Yang Wu, Yuhan Liu, Jinjun Wang, and Sanping Zhou. Event-equalized dense video captioning. InProceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition (CVPR), pages 8417–8427, June

    work page 2025

  27. [27]

    Pllava : Parameter- free llava extension from images to videos for video dense captioning,

    [Xuet al., 2024 ] Lin Xu, Yilin Zhao, Daquan Zhou, Zhijie Lin, See Kiong Ng, and Jiashi Feng. Pllava : Parameter- free llava extension from images to videos for video dense captioning,

    work page 2024

  28. [28]

    Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

    [Zhanget al., 2023 ] Hang Zhang, Xin Li, and Lidong Bing. Video-llama: An instruction-tuned audio-visual lan- guage model for video understanding.arXiv preprint arXiv:2306.02858,

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  29. [29]

    Long-clip: Unlocking the long-text capability of clip,

    [Zhanget al., 2024 ] Beichen Zhang, Pan Zhang, Xiaoyi Dong, Yuhang Zang, and Jiaqi Wang. Long-clip: Un- locking the long-text capability of clip.arXiv preprint arXiv:2403.15378,

    work page arXiv 2024

  30. [30]

    Q-frame: Query-aware frame selection and multi-resolution adaptation for video- llms,

    [Zhanget al., 2025 ] Shaojie Zhang, Jiahui Yang, Jianqin Yin, Zhenbo Luo, and Jian Luan. Q-frame: Query-aware frame selection and multi-resolution adaptation for video- llms,

    work page 2025

  31. [31]

    P Xing, Hao Zhang, Joseph E

    [Zhenget al., 2023 ] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging llm-as-a- judge with mt-bench and chatbot arena,

    work page 2023

  32. [32]

    [Zhouet al., 2018 ] Yipin Zhou, Zhaowen Wang, Chen Fang, Trung Bui, and Tamara L. Berg. Visual to sound: Gen- erating natural sound for videos in the wild. In2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3550–3558,

    work page 2018

  33. [33]

    Focus: Efficient keyframe selection for long video understanding.arXiv preprint arXiv:2510.27280, 2025

    [Zhuet al., 2025 ] Zi-Xuan Zhu, Hailun Xu, Yang Luo, Yong Liu, Kanchan Sarkar, Zhenheng Yang, and Yang You. Fo- cus: Efficient keyframe selection for long video under- standing.ArXiv, abs/2510.27280, 2025

    work page arXiv 2025