📡 Signal & Communications

📹 ICCV2025 · 3 paper notes

Boosting Multimodal Learning via Disentangled Gradient Learning

This paper reveals an optimization conflict between modality encoders and fusion modules in multimodal learning — the fusion module suppresses gradients propagated back to individual modality encoders, causing even the dominant modality to underperform its unimodal counterpart. The paper proposes the Disentangled Gradient Learning (DGL) framework, which addresses this issue by cutting the gradient path from the fusion module to the encoders and replacing it with independent unimodal losses.

Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors

This paper proposes two modules — Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF) — to address material-dependent radiance intensity falloff and frequency-domain denoising under varying SNR conditions in NLOS imaging, respectively. Trained solely on synthetic data, the method achieves state-of-the-art generalization across multiple real-world datasets.

Rectifying Magnitude Neglect in Linear Attention

This paper identifies that Linear Attention completely discards Query magnitude information, causing a significant deviation of attention score distributions from Softmax Attention. It proposes Magnitude-Aware Linear Attention (MALA), which restores magnitude awareness by introducing a scaling factor \(\beta\) and an offset term \(\gamma\), achieving comprehensive improvements over existing methods across classification, detection, segmentation, NLP, speech recognition, and image generation tasks.