Low-rank Decomposition and Adaptation (LoDA) performs a task-driven decomposition to build general and truly task-specific LoRA subspaces by solving two energy-based objectives, decoupling directions for knowledge sharing and isolation.
Ling-Feng He, De Cheng, Huai-Jie Wang et al.· arXiv.org· 12 citations· ⚡2
This work proposes a novel Multimodal-Guided Self-Distillation (MGSD) method for Unified Person Search that leverages multimodal textual descriptions and self-distillation to enhance pedestrian representation learning, and introduces three key innovations.
Xi Yang, He-Xun Zhou, Hai-Yang Zhu et al.· Neural Networks· 0 citations
In semi-supervised object detection (SSOD), due to the limited availability of labeled data, the quality and quantity of pseudo labels generated from unlabeled images are crucial for model training. Our study reveals that in the early stages of training, the number of usable pseudo labels is very low, which hampers the...
Xi Yang, Peng-Hui Li, Nan-Nan Wang· IEEE Transactions on Image P...· 0 citations
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