1 citations · 1 across the 2 of their papers we have counts for
6 papers
Eve: Efficient Multimodal Vision Language Models with Elastic Visual Experts
Miao Rang, Zhenni Bi, Chuanjian Liu +3
Multimodal vision language models (VLMs) have made significant progress with the support of continuously increasing model sizes and data volumes. Running VLMs on edge devices has b…
Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning
Zhenni Bi, Kai Han, Chuanjian Liu +2
Large Language Models (LLMs) have demonstrated remarkable abilities across various language tasks, but solving complex reasoning problems remains a significant challenge. While exi…
Widening and Squeezing: Towards Accurate and Efficient QNNs
Chuanjian Liu, Kai Han, Yunhe Wang +3
Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than th…
Learning Instance-wise Sparsity for Accelerating Deep Models
Chuanjian Liu, Yunhe Wang, Kai Han +2
Exploring deep convolutional neural networks of high efficiency and low memory usage is very essential for a wide variety of machine learning tasks. Most of existing approaches use…
Attribute Aware Pooling for Pedestrian Attribute Recognition
Kai Han, Yunhe Wang, Han Shu +3
This paper expands the strength of deep convolutional neural networks (CNNs) to the pedestrian attribute recognition problem by devising a novel attribute aware pooling algorithm.…
Data-Free Learning of Student Networks
Hanting Chen, Yunhe Wang, Chang Xu +6
Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones…