Rethinking Multi-modal Object Detection from the Perspective of Mono-Modality Feature Learning

• URL: http://arxiv.org/abs/2503.11780v1
• Date: Fri, 14 Mar 2025 18:15:53 GMT
• Title: Rethinking Multi-modal Object Detection from the Perspective of Mono-Modality Feature Learning
• Authors: Tianyi Zhao, Boyang Liu, Yanglei Gao, Yiming Sun, Maoxun Yuan, Xingxing Wei,
• Abstract summary: We introduce linear probing evaluation to the multi-modal detectors and rethink the multi-modal object detection task.<n>We construct an novel framework called M$2$D-LIF, which consists of the Mono-Modality Distillation (M$2$D) method and the Local Illumination-aware Fusion (LIF) module.<n>Our M$2$D-LIF effectively mitigates the Fusion Degradation phenomenon and outperforms the previous SOTA detectors.
• Score: 18.268054258939213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-Modal Object Detection (MMOD), due to its stronger adaptability to various complex environments, has been widely applied in various applications. Extensive research is dedicated to the RGB-IR object detection, primarily focusing on how to integrate complementary features from RGB-IR modalities. However, they neglect the mono-modality insufficient learning problem that the decreased feature extraction capability in multi-modal joint learning. This leads to an unreasonable but prevalent phenomenon--Fusion Degradation, which hinders the performance improvement of the MMOD model. Motivated by this, in this paper, we introduce linear probing evaluation to the multi-modal detectors and rethink the multi-modal object detection task from the mono-modality learning perspective. Therefore, we construct an novel framework called M$^2$D-LIF, which consists of the Mono-Modality Distillation (M$^2$D) method and the Local Illumination-aware Fusion (LIF) module. The M$^2$D-LIF framework facilitates the sufficient learning of mono-modality during multi-modal joint training and explores a lightweight yet effective feature fusion manner to achieve superior object detection performance. Extensive experiments conducted on three MMOD datasets demonstrate that our M$^2$D-LIF effectively mitigates the Fusion Degradation phenomenon and outperforms the previous SOTA detectors.

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