5 papers · 1 filter
SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models
Yaozhi Wen, Jialong Guo, Zhenliang Ni +2
While Vision-Language Models (VLMs) have demonstrated remarkable performance in processing and understanding both text and images, their large parameter sizes lead to significant c…
VLM-Pruner: Buffering for Spatial Sparsity in an Efficient VLM Centrifugal Token Pruning Paradigm
Zhenkai Wu, Xiaowen Ma, Zhenliang Ni +4
Vision-language models (VLMs) excel at image understanding tasks, but the large number of visual tokens imposes significant computational costs, hindering deployment on mobile devi…
TinyViM: Frequency Decoupling for Tiny Hybrid Vision Mamba
Xiaowen Ma, Zhenliang Ni, Xinghao Chen
Mamba has shown great potential for computer vision due to its linear complexity in modeling the global context with respect to the input length. However, existing lightweight Mamb…
SSA-Seg: Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation
Xiaowen Ma, Zhenliang Ni, Xinghao Chen
Vanilla pixel-level classifiers for semantic segmentation are based on a certain paradigm, involving the inner product of fixed prototypes obtained from the training set and pixel…
Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation
Zhenliang Ni, Xinghao Chen, Yingjie Zhai +2
Semantic segmentation is an important task for numerous applications but it is still quite challenging to achieve advanced performance with limited computational costs. In this pap…