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RACANet improves RGB-T crowd counting with reliability-aware fusion

By PulseAugur Editorial · [3 sources] ·

Researchers have developed RACANet, a novel framework for RGB-Thermal crowd counting that improves accuracy by explicitly modeling local spatial discrepancies and modality reliability. The method employs a two-stage approach, beginning with cross-modal alignment pretraining and followed by a Local Anchor Fusion Module. This module leverages reliable regions to generate semantic anchors and adaptively redistributes features using attention mechanisms. Experiments on benchmark datasets show RACANet surpasses existing methods. AI

IMPACT Introduces a new method for improving crowd counting accuracy by integrating visible and thermal data.

RANK_REASON Academic paper introducing a new method for RGB-T crowd counting.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

RACANet improves RGB-T crowd counting with reliability-aware fusion

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COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    RACANet: Reliability-Aware Crowd Anchor Network for RGB-T Crowd Counting

    RGB-Thermal (T) crowd counting aims to integrate visible-spectrum and thermal infrared information to improve the robustness of crowd density estimation in complex scenes. Although existing studies generally improve counting accuracy through cross-modal feature fusion, most curre…

  2. arXiv cs.CV TIER_1 English(EN) · Jinghao Shi, Mengqi Lei, Kunliang He, Yun Li, Wei Bao, Siqi Li ·

    RACANet: Reliability-Aware Crowd Anchor Network for RGB-T Crowd Counting

    arXiv:2604.24543v1 Announce Type: new Abstract: RGB-Thermal (T) crowd counting aims to integrate visible-spectrum and thermal infrared information to improve the robustness of crowd density estimation in complex scenes. Although existing studies generally improve counting accurac…

  3. arXiv cs.CV TIER_1 English(EN) · Siqi Li ·

    RACANet: Reliability-Aware Crowd Anchor Network for RGB-T Crowd Counting

    RGB-Thermal (T) crowd counting aims to integrate visible-spectrum and thermal infrared information to improve the robustness of crowd density estimation in complex scenes. Although existing studies generally improve counting accuracy through cross-modal feature fusion, most curre…

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