arxiv: 2604.17898 · v1 · submitted 2026-04-20 · 💻 cs.CV

Recognition: unknown

ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval

Zixu Li , Yupeng Hu , Zhiwei Chen , Qinlei Huang , Guozhi Qiu , Zhiheng Fu , Meng Liu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:37 UTC · model grok-4.3

classification 💻 cs.CV

keywords composed video retrievalmulti-modal querydirectional bias calibrationsemantic contribution disentanglementevidence-driven alignmentcomposed image retrievalcross-modal retrievalvideo retrieval

0 comments

The pith

ReTrack calibrates directional bias in composed video features by estimating each modality's semantic contribution.

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

The paper proposes ReTrack as a framework for composed video retrieval that counters the tendency of composed features to favor the reference video over the modification text. It does so by disentangling semantic contributions, calibrating the geometry of the composed representation with directional anchors, and using bidirectional evidence to align the query with target videos. This setup targets three specific limitations in prior methods: entangled modality contributions, direct optimization of composed features, and resulting retrieval uncertainty. The same approach extends to composed image retrieval and reports top results on multiple benchmarks. A reader would care because video search often involves users specifying changes to an example clip, and reducing modality imbalance could make such searches more precise.

Core claim

ReTrack is the first CVR framework that improves multi-modal query understanding by calibrating directional bias in composed features. It consists of three key modules: Semantic Contribution Disentanglement, Composition Geometry Calibration, and Reliable Evidence-driven Alignment. Specifically, ReTrack estimates the semantic contribution of each modality to calibrate the directional bias of the composed feature. It then uses the calibrated directional anchors to compute bidirectional evidence that drives reliable composed-to-target similarity estimation. Moreover, ReTrack exhibits strong generalization to the Composed Image Retrieval (CIR) task, achieving SOTA performance across three bench

What carries the argument

The evidence-driven dual-stream directional anchor calibration that estimates semantic contributions of video and text to produce calibrated anchors and bidirectional evidence for similarity computation.

If this is right

Addresses modal contribution entanglement by explicit disentanglement before composition.
Uses calibrated directional anchors and bidirectional evidence to reduce retrieval uncertainty.
Delivers state-of-the-art results on three benchmark datasets for both CVR and CIR.
Provides a reusable code release for the dual-stream calibration modules.

Where Pith is reading between the lines

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

The same bias-calibration logic might apply to other retrieval settings where one modality carries denser information than the other.
Explicit anchor calibration could become a standard preprocessing step in vision-language composition models.
Testing the method on user-generated modification queries outside curated benchmarks would reveal how well the semantic estimates hold in practice.

Load-bearing premise

Estimating the semantic contribution of each modality will calibrate directional bias in the composed feature without introducing new errors.

What would settle it

Retrieval experiments on the standard CVR benchmarks where the calibrated composed features produce no measurable reduction in similarity to the reference video or yield retrieval accuracy no higher than prior composition methods.

Figures

Figures reproduced from arXiv: 2604.17898 by Guozhi Qiu, Meng Liu, Qinlei Huang, Yupeng Hu, Zhiheng Fu, Zhiwei Chen, Zixu Li.

**Figure 2.** Figure 2: The proposed ReTrack consists of three key modules: (a) Semantic Contribution Disentanglement, (b) Composition [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** Figure 4: Case study on (a) WebVid-CoVR and (b) CIRR. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Comprehensive Performance Comparison Be [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

**Figure 6.** Figure 6: More Cases on CVR task. moderately higher for ReTrack, and some variants such as w/o A ref are almost on par with CoVR-2, whereas the standard model incurs about +1.5s per iteration. This overhead is expected and reasonable because ReTrack introduces Semantic Contribution Disentanglement, Composition Geometry Calibration (with dual directional anchors and a direction-aware loss), and Reliable Evidence-d… view at source ↗

**Figure 7.** Figure 7: More Cases on CIR task. tent scenes or produce results dominated by repeated visual elements (e.g., trees) from the reference video, leading to semantic distortion. (a-2) WebVid-CoVR dataset Failure Case: For the modification “change it to cappuccino,” none of the models successfully retrieve the correct result among their top- [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

read the original abstract

With the rapid growth of video data, Composed Video Retrieval (CVR) has emerged as a novel paradigm in video retrieval and is receiving increasing attention from researchers. Unlike unimodal video retrieval methods, the CVR task takes a multi-modal query consisting of a reference video and a piece of modification text as input. The modification text conveys the user's intended alterations to the reference video. Based on this input, the model aims to retrieve the most relevant target video. In the CVR task, there exists a substantial discrepancy in information density between video and text modalities. Traditional composition methods tend to bias the composed feature toward the reference video, which leads to suboptimal retrieval performance. This limitation is significant due to the presence of three core challenges: (1) modal contribution entanglement, (2) explicit optimization of composed features, and (3) retrieval uncertainty. To address these challenges, we propose the evidence-dRivRn dual-sTream diRectionAl anChor calibration networK (ReTrack). ReTrack is the first CVR framework that improves multi-modal query understanding by calibrating directional bias in composed features. It consists of three key modules: Semantic Contribution Disentanglement, Composition Geometry Calibration, and Reliable Evidence-driven Alignment. Specifically, ReTrack estimates the semantic contribution of each modality to calibrate the directional bias of the composed feature. It then uses the calibrated directional anchors to compute bidirectional evidence that drives reliable composed-to-target similarity estimation. Moreover, ReTrack exhibits strong generalization to the Composed Image Retrieval (CIR) task, achieving SOTA performance across three benchmark datasets in both CVR and CIR scenarios. Codes are available at https://github.com/Lee-zixu/ReTrack

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.

Desk Editor's Note private letter to a colleague

ReTrack adds a disentanglement-calibration pipeline to reduce video bias in composed retrieval, but the reliability of that calibration step is not tightly validated.

read the letter

ReTrack is a new dual-stream network for composed video retrieval that estimates per-modality semantic contributions, calibrates directional anchors, and then uses bidirectional evidence for similarity scoring. It targets the density mismatch between video and text queries and shows results on both CVR and CIR benchmarks, with code released. The three modules are presented as the first explicit treatment of modal contribution entanglement, explicit optimization, and retrieval uncertainty in this setting. That framing is useful and the generalization claim to image retrieval is a nice bonus for the community. The architecture is concrete enough that someone could reimplement the core ideas without too much guesswork. The code link helps here too. The soft spot is exactly where the stress test points: the Semantic Contribution Disentanglement module is a learned estimator whose accuracy is not independently checked. If it under-weights the text in high-motion video clips, the subsequent geometry calibration and evidence alignment can lock in the wrong direction rather than correct it. The paper would be stronger with a diagnostic that measures how often the estimated weights match human judgment or with an ablation that isolates the calibration error. The SOTA numbers are reported, but without seeing the full baseline table and error breakdowns it is hard to judge how much comes from the new pieces versus standard tricks like better pre-training or longer training. This paper is for groups already working on multi-modal retrieval or video search interfaces. A reader who needs a practical starting point for handling text modifications on video clips will find the modules and the CIR transfer results worth looking at. I would send it to peer review. The problem is real, the proposed fix is specific, and the experiments appear to be in place even if they need tightening.

Referee Report

2 major / 2 minor

Summary. The paper proposes ReTrack, a dual-stream network for Composed Video Retrieval (CVR) that takes a reference video and modification text as query. It introduces three modules—Semantic Contribution Disentanglement to estimate per-modality semantic contributions, Composition Geometry Calibration to adjust directional anchors in the composed feature, and Reliable Evidence-driven Alignment to compute bidirectional evidence for target similarity—to address modal contribution entanglement, explicit optimization of composed features, and retrieval uncertainty. The central claim is that this calibration of directional bias yields improved multi-modal query understanding, with the method asserted as the first such CVR framework and shown to generalize to Composed Image Retrieval (CIR) with SOTA results on three benchmarks. Code is released at the provided GitHub link.

Significance. If the empirical claims hold after verification, the work would offer a concrete architecture for mitigating modality-density bias in composed retrieval, a practical issue in video-text tasks. The explicit release of code supports reproducibility and is a positive contribution. Generalization to CIR is noted as an additional strength if the cross-task results are robust.

major comments (2)

[Semantic Contribution Disentanglement module (and associated loss)] The central claim that Semantic Contribution Disentanglement calibrates directional bias 'without introducing new errors' (as required to solve the three listed challenges) rests on an unverified assumption. No derivation, orthogonality proof, or independent error bound is supplied showing that the learned contribution estimator does not misattribute semantics (e.g., under-weighting text in dense video regimes) and thereby allow Composition Geometry Calibration or bidirectional alignment to amplify rather than reduce bias.
[Experimental results and evaluation sections] The abstract asserts SOTA performance and strong generalization to CIR across three benchmarks, yet the manuscript provides no quantitative metrics, baseline comparisons, ablation tables, or error analysis that would allow verification of whether the directional-anchor calibration actually drives the reported gains versus other factors.

minor comments (2)

[Abstract] The acronym expansion in the abstract contains inconsistent capitalization ('evidence-dRivRn dual-sTream diRectionAl anChor calibration networK'); standardize to conventional title-case or all-caps form.
[Method overview] Clarify the precise definitions of 'directional anchors' and 'bidirectional evidence' with equations or pseudocode in the method section to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed review of our manuscript. We have carefully considered each major comment and provide point-by-point responses below. Where the comments identify gaps in theoretical justification or presentation, we agree and will revise the manuscript accordingly to strengthen the work.

read point-by-point responses

Referee: [Semantic Contribution Disentanglement module (and associated loss)] The central claim that Semantic Contribution Disentanglement calibrates directional bias 'without introducing new errors' (as required to solve the three listed challenges) rests on an unverified assumption. No derivation, orthogonality proof, or independent error bound is supplied showing that the learned contribution estimator does not misattribute semantics (e.g., under-weighting text in dense video regimes) and thereby allow Composition Geometry Calibration or bidirectional alignment to amplify rather than reduce bias.

Authors: We acknowledge that the manuscript does not provide a formal derivation, orthogonality proof, or explicit error bound for the Semantic Contribution Disentanglement module. The module is motivated by the need to address modal contribution entanglement through a dedicated loss that separates modality-specific semantics, and the overall framework relies on the subsequent Composition Geometry Calibration and Reliable Evidence-driven Alignment to mitigate residual issues. However, we agree that an independent analysis would better support the claim that no new errors are introduced. In the revised manuscript, we will add a dedicated theoretical analysis subsection deriving an error bound for the contribution estimator under varying modality densities and discussing its interaction with the bidirectional alignment to prevent bias amplification. We will also include additional ablation experiments isolating the estimator's behavior in dense-video regimes. revision: yes
Referee: [Experimental results and evaluation sections] The abstract asserts SOTA performance and strong generalization to CIR across three benchmarks, yet the manuscript provides no quantitative metrics, baseline comparisons, ablation tables, or error analysis that would allow verification of whether the directional-anchor calibration actually drives the reported gains versus other factors.

Authors: We thank the referee for this observation. The full manuscript includes Section 4 (Experiments) containing Table 1 with quantitative SOTA comparisons on CVR benchmarks, Table 2 demonstrating generalization to CIR, Section 4.3 with module-wise ablations, and Section 4.4 with error analysis and qualitative examples. These results are intended to show that the directional calibration drives the gains. We agree that the abstract and early sections could more explicitly reference these elements to facilitate verification. In the revision, we will update the abstract, introduction, and conclusion to directly cite the relevant tables and sections, and we will expand the error analysis to more explicitly attribute improvements to the calibration components versus other factors. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical architecture proposal

full rationale

The paper introduces ReTrack as a new neural network framework with three explicitly designed modules (Semantic Contribution Disentanglement, Composition Geometry Calibration, Reliable Evidence-driven Alignment) to address stated CVR challenges. No mathematical derivation chain, equations, or 'predictions' are shown that reduce by construction to fitted inputs, self-definitions, or prior self-citations. Claims rest on the proposed architecture's design and reported benchmark performance rather than tautological reduction. This matches the default case of a non-circular empirical method.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No specific free parameters, axioms, or invented entities identifiable from the abstract alone; the work relies on standard assumptions of neural network training and multi-modal feature composition.

pith-pipeline@v0.9.0 · 5633 in / 985 out tokens · 26106 ms · 2026-05-10T04:37:25.366809+00:00 · methodology

discussion (0)

Reference graph

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