MCoT-MVS: Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval

MCoT-MVS: Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/mcot-mvs-multi-level-vision-selection-by-multi-modal-chain-of-thought-reasoning-for-composed-image-retrieval

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Proof freshness: stale
Proof status: partial
Display score: 9/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID mcot-mvs-multi-level-vision-selection-by-multi-modal-chain-of-thought-reasoning-for-composed-image-retrieval | Route /signal-canvas/mcot-mvs-multi-level-vision-selection-by-multi-modal-chain-of-thought-reasoning-for-composed-image-retrieval

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/mcot-mvs-multi-level-vision-selection-by-multi-modal-chain-of-thought-reasoning-for-composed-image-retrieval

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "mcot-mvs-multi-level-vision-selection-by-multi-modal-chain-of-thought-reasoning-for-composed-image-retrieval",
    "query_text": "Summarize MCoT-MVS: Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "MCoT-MVS: Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval",
  "normalized_query": "2603.17360",
  "route": "/signal-canvas/mcot-mvs-multi-level-vision-selection-by-multi-modal-chain-of-thought-reasoning-for-composed-image-retrieval",
  "paper_ref": "mcot-mvs-multi-level-vision-selection-by-multi-modal-chain-of-thought-reasoning-for-composed-image-retrieval",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 12

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: MCoT-MVS: Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval

PDF: https://arxiv.org/pdf/2603.17360v1

Repository: https://github.com/JJJJerry/WWW2026-MCoT-MVS

Source count: Pending verification

Coverage: 50%

Last proof check: 2026-03-19T21:31:49.672Z

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Ready for execution: MCoT-MVS: Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval

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Subject: MCoT-MVS: Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval

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Last commit

4/15/2026

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Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
we propose a novel Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning (MCoT-MVS) for CIR, integrating attention-aware multi-level vision features guided by reasoning cues from a multi-modal large language model (MLLM).
Implicationmissing
Implication not extracted yet.
Verificationpartial
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Evidencepartial
Specifically, we leverage an MLLM to perform chain-of-thought reasoning on the multimodal composed input, generating the retained, removed, and target-inferred texts.
Implicationmissing
Implication not extracted yet.
Verificationpartial
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Evidencepartial
These textual cues subsequently guide two reference visual attention selection modules to selectively extract discriminative patch-level and instance-level semantics from the reference image.
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Implication not extracted yet.
Verificationpartial
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Evidencepartial
Finally, to effectively fuse these multi-granular visual cues with the modified text and the imagined target description, we design a weighted hierarchical combination module to align the composed query with target images in a unified embedding space.
Implicationmissing
Implication not extracted yet.
Verificationpartial
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Evidencepartial
Extensive experiments on two CIR benchmarks, namely CIRR and FashionIQ, demonstrate that our approach consistently outperforms existing methods and achieves new state-of-the-art performance.
Implicationmissing
Implication not extracted yet.
Verificationpartial
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Evidencepartial
However, existing methods often struggle to extract the correct semantic cues from the reference image that best reflect the user's intent under textual modification prompts, resulting in interference from irrelevant visual noise.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
to align the composed query with target images in a unified embedding space.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
Code and trained models are publicly released.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
Extensive experiments on two CIR benchmarks, namely CIRR and FashionIQ, demonstrate that our approach consistently outperforms existing methods and achieves new state-of-the-art performance.
Implicationpartial
Explicitly stated in abstract with benchmark names and performance claim
Verificationpartial
partial
Evidencepartial
However, existing methods often struggle to extract the correct semantic cues from the reference image that best reflect the user's intent under textual modification prompts, resulting in interference from irrelevant visual noise.
Implicationpartial
Directly stated as problem statement in abstract
Verificationpartial
partial
Evidencepartial
Specifically, we leverage an MLLM to perform chain-of-thought reasoning on the multimodal composed input, generating the retained, removed, and target-inferred texts.
Implicationpartial
Explicitly described as core method component in abstract
Verificationpartial
partial
Evidencepartial
These textual cues subsequently guide two reference visual attention selection modules to selectively extract discriminative patch-level and instance-level semantics from the reference image.
Implicationpartial
Directly stated as technical approach in abstract
Verificationpartial
partial

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MCoT-MVS: Multi-level Vision Selection by Multi-modal Chain-of-Thought Reasoning for Composed Image Retrieval

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