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cs.CV2025★ 1 cited
Enhanced Structured Lasso Pruning with Class-wise Information
Xiang Liu, Mingchen Li, Xia Li +7
Modern applications require lightweight neural network models. Most existing neural network pruning methods focus on removing unimportant filters; however, these may result in the…
cs.CV2024
SILMM: Self-Improving Large Multimodal Models for Compositional Text-to-Image Generation
Leigang Qu, Haochuan Li, Wenjie Wang +4
Large Multimodal Models (LMMs) have demonstrated impressive capabilities in multimodal understanding and generation, pushing forward advancements in text-to-image generation. Howev…
cs.CV2024★ 1 cited
Efficient Partitioning Vision Transformer on Edge Devices for Distributed Inference
Xiang Liu, Yijun Song, Xia Li +5
Deep learning models are increasingly utilized on resource-constrained edge devices for real-time data analytics. Recently, Vision Transformer and their variants have shown excepti…