261 citations · 292 across the 15 of their papers we have counts for
15 papers
Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
Zhijun Tu, Jian Li, Yuanyuan Xi +5
1-bit LLM quantization offers significant advantages in reducing storage and computational costs. However, existing methods typically train 1-bit LLMs from scratch, failing to full…
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…
EMS-SD: Efficient Multi-sample Speculative Decoding for Accelerating Large Language Models
Yunsheng Ni, Chuanjian Liu, Yehui Tang +2
Speculative decoding emerges as a pivotal technique for enhancing the inference speed of Large Language Models (LLMs). Despite recent research aiming to improve prediction efficien…
An Empirical Study of Scaling Law for OCR
Miao Rang, Zhenni Bi, Chuanjian Liu +2
The laws of model size, data volume, computation and model performance have been extensively studied in the field of Natural Language Processing (NLP). However, the scaling laws in…
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
Chengcheng Wang, Wei He, Ying Nie +4
In the past years, YOLO-series models have emerged as the leading approaches in the area of real-time object detection. Many studies pushed up the baseline to a higher level by mod…
Boosting Semantic Segmentation from the Perspective of Explicit Class Embeddings
Yuhe Liu, Chuanjian Liu, Kai Han +2
Semantic segmentation is a computer vision task that associates a label with each pixel in an image. Modern approaches tend to introduce class embeddings into semantic segmentation…
Category Feature Transformer for Semantic Segmentation
Quan Tang, Chuanjian Liu, Fagui Liu +5
Aggregation of multi-stage features has been revealed to play a significant role in semantic segmentation. Unlike previous methods employing point-wise summation or concatenation f…
Bi-ViT: Pushing the Limit of Vision Transformer Quantization
Yanjing Li, Sheng Xu, Mingbao Lin +4
Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying large pre-trained networks on resource-limited devices. Fully-binarized ViTs (Bi-ViT) th…
Redistribution of Weights and Activations for AdderNet Quantization
Ying Nie, Kai Han, Haikang Diao +3
Adder Neural Network (AdderNet) provides a new way for developing energy-efficient neural networks by replacing the expensive multiplications in convolution with cheaper additions…
Greedy Network Enlarging
Chuanjian Liu, Kai Han, An Xiao +4
Recent studies on deep convolutional neural networks present a simple paradigm of architecture design, i.e., models with more MACs typically achieve better accuracy, such as Effici…
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…