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pith:XGHV5MA2

pith:2026:XGHV5MA2AGK7S56AY6WGANHN46

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WD-FQDet: Multispectral Detection Transformer via Wavelet Decomposition and Frequency-aware Query Learning

Chunjin Yang, Fanman Meng, Xiwei Zhang, Yiming Xiao

Wavelet decomposition decouples shared low-frequency and specific high-frequency features from infrared and visible images to improve object detection.

arxiv:2605.13621 v1 · 2026-05-13 · cs.CV

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Claims

C1strongest claim

we propose a novel detection framework WD-FQDet that explicitly decouples modality-shared and modality-specific information from infrared and visible modalities in the new view of low- and high-frequency domains, allowing fusion strategies tailored to their frequency characteristics... Experimental results on the FLIR, LLVIP, and M3FD datasets demonstrate that WD-FQDet achieves state-of-the-art performance across multiple evaluation metrics.

C2weakest assumption

The assumption that wavelet decomposition cleanly separates modality-shared low-frequency features from modality-specific high-frequency features and that the proposed alignment, retention, and query modules will mitigate bias and insufficiency without introducing artifacts or overfitting to the specific datasets.

C3one line summary

WD-FQDet decouples modality-shared and modality-specific features in infrared-visible images via wavelet-based frequency decomposition and frequency-aware query selection to achieve state-of-the-art detection performance.

References

63 extracted · 63 resolved · 2 Pith anchors

[1] Multi-modality medical im- age fusion using discrete wavelet transform.Procedia Com- puter Science, 70:625–631, 2015 2015

[2] Multimodal object detection by channel switching and spatial attention 2023

[3] End-to- end object detection with transformers 2020

[4] Timothy Chase Jr, Chris Gnam, John Crassidis, and Karthik Dantu. You only crash once: Improved object detection for real-time, sim-to-real hazardous terrain detection and classi- fication for autonomo 2023

[5] Multimodal object detection via probabilistic ensembling 2022

Receipt and verification

First computed	2026-05-18T02:44:17.877587Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

b98f5eb01a0195f977c0c7ac6034ede7bd3b86046d89ce27824c428b3f7eb12c

Aliases

arxiv: 2605.13621 · arxiv_version: 2605.13621v1 · doi: 10.48550/arxiv.2605.13621 · pith_short_12: XGHV5MA2AGK7 · pith_short_16: XGHV5MA2AGK7S56A · pith_short_8: XGHV5MA2

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/XGHV5MA2AGK7S56AY6WGANHN46 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: b98f5eb01a0195f977c0c7ac6034ede7bd3b86046d89ce27824c428b3f7eb12c

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T14:51:05Z",
    "title_canon_sha256": "f49fed222480a2be2aec22a7f9fc42fefd6c2b4e8fb2c75361670da6e9fe1114"
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