Pith. sign in

REVIEW 2 major objections 7 cited by

A temporal transformer on CLIP frame features plus two-stage reranking improves egocentric video retrieval on soft-label benchmarks.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.3

2026-06-30 14:16 UTC pith:JZW7A3YY

load-bearing objection Incremental challenge report adds temporal transformer and reranking to CLIP for EK-100 MIR but supplies no ablations or baselines to back the effectiveness claim. the 2 major comments →

arxiv 2605.24470 v3 pith:JZW7A3YY submitted 2026-05-23 cs.CV

TempRet: Temporal Enhancement and Two-Stage Reranking for CVPR 2026 EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge

Zixu Li , Yupeng Hu , Zhiwei Chen , Zhiheng Fu , Xiaowei Zhu , Weili Guan , Liqiang Nie This is my paper
classification cs.CV
keywords egocentric video retrievalmulti-instance retrievaltemporal transformertwo-stage rerankingCLIP featuresEPIC-KITCHENS-100video-text retrievalsoft labels
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper introduces TempRet to address the assumption in most video-text retrieval methods that visual semantics are captured sufficiently frame-by-frame. It adds a temporal transformer that models inter-frame dependencies using learnable positional encodings and multi-head self-attention applied only to the video side of a CLIP dual-encoder. A two-stage reranking pipeline then refines the top-K candidates from the dual-encoder using a cross-encoder with an Image-Text Matching head. Training employs Symmetric Multi-Similarity Loss to make use of the graded relevance matrices in the EPIC-KITCHENS-100 MIR challenge. The resulting system reports 67.97 percent average mAP and 82.92 percent average nDCG.

Core claim

The central claim is that a temporal transformer operating exclusively on frame-level CLIP features, combined with a two-stage reranking pipeline that applies an ITM head to top-K candidates, produces measurable gains on the EK-100 MIR benchmark by capturing temporal dynamics and resolving graded cross-modal correspondences, reaching 67.97 percent average mAP and 82.92 percent average nDCG when trained with Symmetric Multi-Similarity Loss on the provided soft-label matrices.

What carries the argument

The temporal transformer with learnable positional encodings and multi-head self-attention on frame-level CLIP features, paired with a dual-encoder followed by a cross-encoder reranking stage using an ITM head.

Load-bearing premise

That operating a temporal transformer exclusively on frame-level CLIP features combined with a two-stage reranking pipeline using an ITM head will produce measurable gains on the challenge's soft-label relevance matrices.

What would settle it

A controlled ablation on the EK-100 MIR test set in which the temporal transformer or the reranking stage is removed and the resulting mAP or nDCG shows no drop or an increase would falsify the claim that these components drive the reported gains.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Temporal modeling on the video side addresses the frame-by-frame limitation inherited from image-text retrieval methods.
  • The two-stage pipeline separates efficient initial retrieval from more expensive cross-modal refinement for graded labels.
  • Symmetric Multi-Similarity Loss directly exploits the soft relevance matrices rather than forcing binary decisions.
  • The approach targets egocentric kitchen videos where multiple instances and temporal order matter for retrieval.

Where Pith is reading between the lines

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

  • The same temporal enhancement could be tested on other video domains that require timing awareness beyond static frames.
  • Replacing the CLIP backbone with a stronger video encoder might compound the gains from the temporal transformer.
  • An end-to-end version that folds the temporal transformer into the cross-encoder stage could reduce the need for separate reranking.
  • The soft-label handling via Symmetric Multi-Similarity Loss may transfer to other retrieval tasks that supply graded relevance data.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 0 minor

Summary. The paper presents TempRet for the EPIC-KITCHENS-100 Multi-Instance Retrieval challenge. It augments a CLIP dual-encoder with a temporal transformer (learnable positional encodings and multi-head self-attention on frame-level features) on the video side and a two-stage reranking pipeline (Top-K dual-encoder retrieval followed by cross-encoder ITM refinement). The system is trained end-to-end with Symmetric Multi-Similarity Loss on the challenge's soft-label relevance matrices and reports 67.97% average mAP and 82.92% average nDCG.

Significance. If the reported scores can be shown to arise from the temporal and reranking components rather than training details alone, the work would supply concrete evidence on the value of explicit temporal modeling and cross-modal refinement for egocentric video retrieval under graded relevance. The absence of any baseline or ablation numbers, however, prevents evaluation of whether these additions produce measurable gains on the soft-label task.

major comments (2)
  1. [Abstract] Abstract: the central claim that the reported 67.97% mAP / 82.92% nDCG 'demonstrate the effectiveness of temporal modeling and cross-modal refinement' is unsupported because the manuscript supplies neither a plain CLIP dual-encoder baseline nor any component-wise ablations (temporal transformer removed, reranking stage removed). Without these controls it is impossible to attribute performance to the proposed modules rather than the loss or training procedure.
  2. [Abstract] Abstract / Results: no training details, validation splits, hyper-parameter settings, or error analysis are provided. The empirical outcomes therefore cannot be assessed for robustness or reproducibility on the EK-100 MIR soft-label matrices.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments below and will revise the manuscript to strengthen the empirical support and reproducibility.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the reported 67.97% mAP / 82.92% nDCG 'demonstrate the effectiveness of temporal modeling and cross-modal refinement' is unsupported because the manuscript supplies neither a plain CLIP dual-encoder baseline nor any component-wise ablations (temporal transformer removed, reranking stage removed). Without these controls it is impossible to attribute performance to the proposed modules rather than the loss or training procedure.

    Authors: We agree that the abstract claim would be better supported by explicit baselines and ablations. In the revised manuscript we will add results for a plain CLIP dual-encoder baseline together with component ablations (temporal transformer removed; reranking stage removed) so that performance gains can be attributed to the proposed modules. revision: yes

  2. Referee: [Abstract] Abstract / Results: no training details, validation splits, hyper-parameter settings, or error analysis are provided. The empirical outcomes therefore cannot be assessed for robustness or reproducibility on the EK-100 MIR soft-label matrices.

    Authors: We acknowledge that these details are required for reproducibility. The revised version will include the training procedure, validation splits, all hyper-parameter settings, and any error analysis performed. revision: yes

Circularity Check

0 steps flagged

Empirical challenge report with no derivation chain or fitted predictions

full rationale

The paper describes an architecture (temporal transformer on CLIP features plus two-stage ITM reranking) trained with Symmetric Multi-Similarity Loss and reports the resulting mAP/nDCG numbers on the EK-100 MIR benchmark. No equations, first-principles derivations, uniqueness theorems, or 'predictions' are claimed; the performance figures are direct empirical outcomes of running the system on the challenge data. No self-citations, ansatzes, or renamings appear in the provided text that could reduce any step to its own inputs by construction. This is a standard empirical method report whose central claim is supported (or not) by the benchmark evaluation itself rather than by any internal reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no explicit free parameters, axioms, or invented entities; the approach relies on pre-existing CLIP models, standard transformer attention, and a symmetric multi-similarity loss from prior work.

pith-pipeline@v0.9.1-grok · 5833 in / 1158 out tokens · 54768 ms · 2026-06-30T14:16:21.898737+00:00 · methodology

0 comments
read the original abstract

Video-text retrieval has witnessed remarkable progress driven by large-scale vision-language pretraining, yet most existing approaches inherit an implicit assumption from image-text retrieval: that visual semantics can be captured frame-by-frame. This assumption overlooks the temporal dynamics of egocentric videos. The EPIC-KITCHENS-100 Multi-Instance Retrieval (MIR) challenge further raises the bar by providing soft-label relevance matrices rather than binary labels, demanding models that can resolve graded semantic correspondences across modalities. In this report, we present our solution, termed TempRet, to the CVPR 2026 EPIC-KITCHENS-100 MIR challenge. Our approach builds upon a CLIP-based dual-encoder backbone and introduces two key components to address the temporal and cross-modal challenges. First, a temporal transformer operates exclusively on the video side, modeling inter-frame dependencies through learnable positional encodings and multi-head self-attention over frame-level CLIP features. Second, a two-stage reranking pipeline first retrieves Top-K candidates via the dual-encoder, then refines their scores using a cross-encoder equipped with an Image-Text Matching (ITM) head. The entire system is trained with Symmetric Multi-Similarity Loss to exploit the soft-label relevance matrices provided by the challenge. Our method achieves 67.97% average mAP and 82.92% average nDCG on the EK-100 MIR benchmark, demonstrating the effectiveness of temporal modeling and cross-modal refinement for egocentric video retrieval.

Figures

Figures reproduced from arXiv: 2605.24470 by Liqiang Nie, Weili Guan, Xiaowei Zhu, Yupeng Hu, Zhiheng Fu, Zhiwei Chen, Zixu Li.

Figure 1
Figure 1. Figure 1: Pipeline of TempRet. In the visual branch, sampled video frames are encoded independently by CLIP image encoder, and passed [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

discussion (0)

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

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. COMBINER: Composed Image Retrieval Guided by Attribute-based Neighbor Relations

    cs.CV 2026-06 unverdicted novelty 6.0

    COMBINER proposes a new architecture for composed image retrieval using adaptive semantic disentanglement, unified prototype-based composition, and dual attribute-based relation modeling to address visually similar bu...

  2. R^3: Composed Video Retrieval via Reasoning-Guided Recalling and Re-ranking

    cs.CV 2026-05 unverdicted novelty 5.0

    R^3 is a zero-shot pipeline that generates reasoning traces to augment composed video queries, fuses scores via agreement-gated residual, and re-ranks candidates for the CoVR-R challenge.

  3. RankVR: Low-Rank Structure Perception and Value Recalibration for Robust Composed Image Retrieval

    cs.CV 2026-06 unverdicted novelty 4.0

    RankVR introduces GSCP and ASVC modules to improve CIR robustness by decoupling clean samples via low-rank structure and dynamically scoring triplet value in noisy datasets.

  4. IMAGINE: Adaptive Schema-Imagery Enhanced Composition for Composed Video Retrieval

    cs.CV 2026-06 unverdicted novelty 4.0

    IMAGINE uses adaptive schema-imagery via dynamic multimodal prototypes to incorporate implicit semantics into composed video retrieval, claiming SOTA results on CVR and CIR benchmarks.

  5. EgoAdapt: A Multi-Scene Egocentric Adaptation Method for CVPR 2026 HD-EPIC VQA Challenge

    cs.CV 2026-05 unverdicted novelty 3.0

    EgoAdapt improves VQA on the HD-EPIC egocentric benchmark via category-conditioned routing, calibrated option scoring, and test-time consistency adaptation.

  6. EgoAction: Egocentric Action Composition with Reliability-Aware Temporal Fusion for the EPIC-KITCHENS Action Detection Challenge at CVPR 2026

    cs.CV 2026-05 unverdicted novelty 3.0

    EgoAction uses decoupled verb-noun temporal detectors on VideoMAE features and Dynamic Weighted Fusion of boundaries based on classification confidences for the EPIC-KITCHENS action detection challenge.

  7. OmniEgo-R$^2$: A Routed Reasoning Framework for the 1st Cross-Domain EgoCross Challenge at CVPR 2026

    cs.CV 2026-05 unverdicted novelty 3.0

    OmniEgo-R² is a competition system that combines domain-specific VL models with temporal normalization, capability routing, and answer calibration to reach 66.35-66.77% accuracy on the EgoCross challenge.

Reference graph

Works this paper leans on

29 extracted references · 5 canonical work pages · cited by 7 Pith papers · 4 internal anchors

  1. [1]

    Encoder: Entity mining and modifica- tion relation binding for composed image retrieval

    Zixu Li, Zhiwei Chen, Haokun Wen, Zhiheng Fu, Yupeng Hu, and Weili Guan. Encoder: Entity mining and modifica- tion relation binding for composed image retrieval. InAAAI, pages 5101–5109, 2025. 1

    2025

  2. [2]

    Offset: Segmentation-based focus shift revision for composed image retrieval

    Zhiwei Chen, Yupeng Hu, Zixu Li, Zhiheng Fu, Xuemeng Song, and Liqiang Nie. Offset: Segmentation-based focus shift revision for composed image retrieval. InACM MM, page 6113–6122, 2025

    2025

  3. [3]

    Deep image retrieval: Learning global representations for image search

    Albert Gordo, Jon Almaz ´an, Jerome Revaud, and Diane Lar- lus. Deep image retrieval: Learning global representations for image search. InECCV, pages 241–257. Springer, 2016

    2016

  4. [4]

    Hint: Com- posed image retrieval with dual-path compositional contex- tualized network

    Mingyu Zhang, Zixu Li, Zhiwei Chen, Zhiheng Fu, Xiaowei Zhu, Jiajia Nie, Yinwei Wei, and Yupeng Hu. Hint: Com- posed image retrieval with dual-path compositional contex- tualized network. InICASSP, pages 13002–13006. IEEE, 2026

    2026

  5. [5]

    Uniter: Universal image-text representation learning

    Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. InECCV, pages 104–120. Springer, 2020

    2020

  6. [6]

    Melt: Improve com- posed image retrieval via the modification frequentation- rarity balance network

    Guozhi Qiu, Zhiwei Chen, Zixu Li, Qinlei Huang, Zhiheng Fu, Xuemeng Song, and Yupeng Hu. Melt: Improve com- posed image retrieval via the modification frequentation- rarity balance network. InICASSP, pages 13007–13011. IEEE, 2026

    2026

  7. [7]

    Median: Adaptive intermediate-grained aggregation network for composed im- age retrieval

    Qinlei Huang, Zhiwei Chen, Zixu Li, Chunxiao Wang, Xue- meng Song, Yupeng Hu, and Liqiang Nie. Median: Adaptive intermediate-grained aggregation network for composed im- age retrieval. InICASSP, pages 1–5. IEEE, 2025. 1

    2025

  8. [8]

    Covr: Learning composed video retrieval from web video captions

    Lucas Ventura, Antoine Yang, Cordelia Schmid, and G ¨ul Varol. Covr: Learning composed video retrieval from web video captions. InAAAI, pages 5270–5279, 2024. 1

    2024

  9. [9]

    Hud: Hierarchical uncertainty-aware disambiguation network for composed video retrieval

    Zhiwei Chen, Yupeng Hu, Zixu Li, Zhiheng Fu, Haokun Wen, and Weili Guan. Hud: Hierarchical uncertainty-aware disambiguation network for composed video retrieval. In ACM MM, page 6143–6152, 2025

    2025

  10. [10]

    Multi-modal transformer for video retrieval

    Valentin Gabeur, Chen Sun, Karteek Alahari, and Cordelia Schmid. Multi-modal transformer for video retrieval. In ECCV, pages 214–229. Springer, 2020

    2020

  11. [11]

    Refine: Composed video retrieval via shared and differential semantics enhancement

    Yupeng Hu, Zixu Li, Zhiwei Chen, Qinlei Huang, Zhiheng Fu, Mingzhu Xu, and Liqiang Nie. Refine: Composed video retrieval via shared and differential semantics enhancement. ACM ToMM, 2026

    2026

  12. [12]

    CoVR-R:Reason-Aware Composed Video Retrieval

    Omkar Thawakar, Dmitry Demidov, Vaishnav Potlapalli, Sai Prasanna Teja Reddy Bogireddy, Viswanatha Reddy Gajjala, Alaa Mostafa Lasheen, Rao Muhammad Anwer, and Fa- had Khan. Covr-r: Reason-aware composed video retrieval. arXiv preprint arXiv:2603.20190, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  13. [13]

    Retrack: Evidence-driven dual-stream directional anchor calibration network for com- posed video retrieval

    Zixu Li, Yupeng Hu, Zhiwei Chen, Qinlei Huang, Guozhi Qiu, Zhiheng Fu, and Meng Liu. Retrack: Evidence-driven dual-stream directional anchor calibration network for com- posed video retrieval. InAAAI, pages 23373–23381, 2026. 1

    2026

  14. [14]

    Rescaling egocentric vision: Collection, pipeline and chal- lenges for epic-kitchens-100.IJCV, 130(1):33–55, 2022

    Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Evangelos Kazakos, Jian Ma, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, et al. Rescaling egocentric vision: Collection, pipeline and chal- lenges for epic-kitchens-100.IJCV, 130(1):33–55, 2022. 1

    2022

  15. [15]

    Stable: Efficient hybrid nearest neighbor search via magnitude-uniformity and cardinality- robustness.IEEE TKDE, 2026

    Qianyun Yang, Zhiwei Chen, Yupeng Hu, Zixu Li, Zhi- heng Fu, and Liqiang Nie. Stable: Efficient hybrid nearest neighbor search via magnitude-uniformity and cardinality- robustness.IEEE TKDE, 2026. 1

    2026

  16. [16]

    Erase: Bypassing collaborative detection of ai counterfeit via com- prehensive artifacts elimination.IEEE TDSC, pages 1–18,

    Qianyun Yang, Peizhuo Lv, Yingjiu Li, Shengzhi Zhang, Yuxuan Chen, Zhiwei Chen, Zixu Li, and Yupeng Hu. Erase: Bypassing collaborative detection of ai counterfeit via com- prehensive artifacts elimination.IEEE TDSC, pages 1–18,

  17. [17]

    Learn- ing transferable visual models from natural language super- vision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learn- ing transferable visual models from natural language super- vision. InICML, pages 8748–8763. PmLR, 2021. 1

    2021

  18. [18]

    Pair: Complementarity-guided disentanglement for composed im- age retrieval

    Zhiheng Fu, Zixu Li, Zhiwei Chen, Chunxiao Wang, Xuemeng Song, Yupeng Hu, and Liqiang Nie. Pair: Complementarity-guided disentanglement for composed im- age retrieval. InICASSP, pages 1–5. IEEE, 2025. 1

    2025

  19. [19]

    Sam-clip: Merging vision foundation models to- wards semantic and spatial understanding

    Haoxiang Wang, Pavan Kumar Anasosalu Vasu, Fartash Faghri, Raviteja Vemulapalli, Mehrdad Farajtabar, Sachin Mehta, Mohammad Rastegari, Oncel Tuzel, and Hadi Pouransari. Sam-clip: Merging vision foundation models to- wards semantic and spatial understanding. InCVPR, pages 3635–3647, 2024

    2024

  20. [20]

    Habit: Chrono- synergia robust progressive learning framework for com- posed image retrieval

    Zixu Li, Yupeng Hu, Zhiwei Chen, Shiqi Zhang, Qinlei Huang, Zhiheng Fu, and Yinwei Wei. Habit: Chrono- synergia robust progressive learning framework for com- posed image retrieval. InAAAI, pages 6762–6770, 2026

    2026

  21. [21]

    Clip-reid: exploiting vision-language model for image re-identification without concrete text labels

    Siyuan Li, Li Sun, and Qingli Li. Clip-reid: exploiting vision-language model for image re-identification without concrete text labels. InAAAI, pages 1405–1413, 2023

    2023

  22. [22]

    Intent: Invariance and discrimination-aware noise mitigation for robust composed image retrieval

    Zhiwei Chen, Yupeng Hu, Zhiheng Fu, Zixu Li, Jiale Huang, Qinlei Huang, and Yinwei Wei. Intent: Invariance and discrimination-aware noise mitigation for robust composed image retrieval. InAAAI, pages 20463–20471, 2026. 1

    2026

  23. [23]

    FineCIR: Explicit parsing of fine-grained modification semantics for composed image retrieval,

    Zixu Li, Zhiheng Fu, Yupeng Hu, Zhiwei Chen, Haokun Wen, and Liqiang Nie. Finecir: Explicit parsing of fine- grained modification semantics for composed image re- trieval.https://arxiv.org/abs/2503.21309, 2025. 1

    work page arXiv 2025

  24. [24]

    Temporal action detection with structured segment networks

    Yue Zhao, Yuanjun Xiong, Limin Wang, Zhirong Wu, Xi- aoou Tang, and Dahua Lin. Temporal action detection with structured segment networks. InICCV, pages 2914–2923, 2017

    2017

  25. [25]

    TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval

    Zixu Li, Yupeng Hu, Zhiheng Fu, Zhiwei Chen, Yongqi Li, and Liqiang Nie. Tema: Anchor the image, follow the text for multi-modification composed image retrieval.arXiv preprint arXiv:2604.21806, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  26. [26]

    Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval

    Zhiheng Fu, Yupeng Hu, Qianyun Yang, Shiqi Zhang, Zhi- wei Chen, and Zixu Li. Air-know: Arbiter-calibrated knowledge-internalizing robust network for composed image retrieval.arXiv preprint arXiv:2604.19386, 2026. 1

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  27. [27]

    Symmetric multi-similarity loss for epic-kitchens-100 multi-instance re- trieval challenge 2024, 2024

    Xiaoqi Wang, Yi Wang, and Lap-Pui Chau. Symmetric multi-similarity loss for epic-kitchens-100 multi-instance re- trieval challenge 2024, 2024. 2, 3

    2024

  28. [28]

    Global features are all you need for image retrieval and reranking

    Shihao Shao, Kaifeng Chen, Arjun Karpur, Qinghua Cui, Andr´e Araujo, and Bingyi Cao. Global features are all you need for image retrieval and reranking. InICCV, pages 11036–11046, 2023. 2

    2023

  29. [29]

    ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval

    Zixu Li, Yupeng Hu, Zhiwei Chen, Mingyu Zhang, Zhiheng Fu, and Liqiang Nie. Conesep: Cone-based robust noise- unlearning compositional network for composed image re- trieval.arXiv preprint arXiv:2604.20358, 2026. 2

    work page internal anchor Pith review Pith/arXiv arXiv 2026