14 citations · 23 across the 7 of their papers we have counts for
4 papers · 1 filter
MODAL: Multi-Modal Object Re-ID via Model-Driven Sparse Decoupling and Text-Image Differential Filtering
Chengbo Huang, Jun-Jie Huang, Long Lan +5
Multi-modal object re-identification (Re-ID) aims to facilitate cross-camera object retrieval in complex environments by leveraging complementary information from visual (e.g., RGB…
AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs
Wenxiang Lin, Juntao Huang, Luhan Zhang +5
Quantization is a key method for reducing the GPU memory requirement of training large language models (LLMs). Yet, current approaches are ineffective for 4-bit activations and 8-b…
EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure
Zihao Ding, Beining Wu, Jun Huang
Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint embedding training entangles…
Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion
Ge Luo, Jun-Jie Huang, Qi Yu +6
Deep Unfolding Network-based methods have emerged as effective solutions for multi-source image fusion by combining model-driven iterative optimization with data-driven deep learni…