Learnable Instance Attention Filtering for Adaptive Detector Distillation

Learnable Instance Attention Filtering for Adaptive Detector Distillation | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/learnable-instance-attention-filtering-for-adaptive-detector-distillation

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 41
Source count: 3
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 learnable-instance-attention-filtering-for-adaptive-detector-distillation | Route /signal-canvas/learnable-instance-attention-filtering-for-adaptive-detector-distillation

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/learnable-instance-attention-filtering-for-adaptive-detector-distillation

MCP example

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}

source_context

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  "benchmark_ref": null,
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Evidence Receipt

Route status: building

Claims: 12

References: 41

Proof: Verification pending

Freshness state: computing

Source paper: Learnable Instance Attention Filtering for Adaptive Detector Distillation

PDF: https://arxiv.org/pdf/2603.26088v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T22:24:40.766Z

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Watch and verify: Learnable Instance Attention Filtering for Adaptive Detector Distillation

/buildability/learnable-instance-attention-filtering-for-adaptive-detector-distillation

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Subject: Learnable Instance Attention Filtering for Adaptive Detector Distillation

Verdict

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Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
we propose Learnable Instance Attention Filtering for Adaptive Detector Distillation (LIAF-KD), a novel framework that introduces learnable instance selectors to dynamically evaluate and reweight instance importance during distillation.
Implicationpartial
This is a core methodological contribution explicitly stated in the abstract and conclusion.
Verificationpartial
partial
Evidencepartial
Notably, the student contributes to this process based on its evolving learning state.
Implicationpartial
This is a key feature of the proposed method, explicitly mentioned in the abstract and conclusion.
Verificationpartial
partial
Evidencepartial
Experiments on the KITTI and COCO datasets demonstrate consistent improvements, with a 2% gain on a GFL ResNet-50 student without added complexity, outperforming state-of-the-art methods.
Implicationpartial
This is a specific quantitative result reported in the abstract and supported by Table 1.
Verificationpartial
partial
Evidencepartial
Experiments on the KITTI and COCO datasets demonstrate consistent improvements, with a 2% gain on a GFL ResNet-50 student without added complexity, outperforming state-of-the-art methods.
Implicationpartial
The abstract states this, and Table 1 provides comparative results showing LIAF-KD outperforming other methods.
Verificationpartial
partial
Evidencepartial
In contrast, LIAF-KD achieves more accurate object localization and classification.
Implicationpartial
This is a qualitative result supported by the visual comparison in Figure 2.
Verificationpartial
partial
Evidencepartial
Moreover, existing attention filtering mechanisms are typically heuristic or teacher-driven, rather than learned with the student.
Implicationpartial
This is presented as a limitation of prior work, motivating the proposed method, and is explicitly stated in the abstract.
Verificationpartial
partial
Evidencepartial
The proposed method consistently outperforms student baselines and several state-of-the-art KD approaches while maintaining a favorable complexity–accuracy trade-off, highlighting its potential for real-time and resource-constrained applications.
Implicationpartial
This is a key benefit and implication of the method, stated in the conclusion.
Verificationpartial
partial
Evidencepartial
we propose Learnable Instance Attention Filtering for Adaptive Detector Distillation (LIAF-KD), a novel framework that introduces learnable instance selectors to dynamically evaluate and reweight instance importance during distillation.
Implicationpartial
This is a core methodological contribution explicitly stated in the abstract and conclusion.
Verificationpartial
partial
Evidencepartial
Notably, the student contributes to this process based on its evolving learning state.
Implicationpartial
This is a key differentiator of the proposed method, explicitly mentioned in the abstract and conclusion.
Verificationpartial
partial
Evidencepartial
Experiments on the KITTI and COCO datasets demonstrate consistent improvements, with a 2% gain on a GFL ResNet-50 student without added complexity, outperforming state-of-the-art methods.
Implicationpartial
This is a specific quantitative result reported in the abstract and supported by Table 1.
Verificationpartial
partial
Evidencepartial
Experiments on the KITTI and COCO datasets demonstrate consistent improvements, with a 2% gain on a GFL ResNet-50 student without added complexity, outperforming state-of-the-art methods.
Implicationpartial
The abstract states this, and Table 1 shows LIAF-KD achieving higher mAP than other methods like MasKD, GID, FitNet, and DeFeat.
Verificationpartial
partial
Evidencepartial
In contrast, LIAF-KD achieves more accurate object localization and classification.
Implicationpartial
This is a qualitative result supported by visual comparison in Figure 2.
Verificationpartial
partial

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Learnable Instance Attention Filtering for Adaptive Detector Distillation

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