4 citations · 12 across the 10 of their papers we have counts for
11 papers
OmniSparse: Training-Aware Fine-Grained Sparse Attention for Long-Video MLLMs
Feng Chen, Yefei He, Shaoxuan He +9
Existing sparse attention methods primarily target inference-time acceleration by selecting critical tokens under predefined sparsity patterns. However, they often fail to bridge t…
ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification
Yefei He, Feng Chen, Jing Liu +4
The efficiency of large vision-language models (LVLMs) is constrained by the computational bottleneck of the attention mechanism during the prefill phase and the memory bottleneck…
ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models
Jing Liu, Ruihao Gong, Mingyang Zhang +3
LLM development involves pre-training a foundation model on massive data, followed by fine-tuning on task-specific data to create specialized experts. Serving these experts can pos…
ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token Identification
Yefei He, Luoming Zhang, Weijia Wu +3
KV cache stores key and value states from previous tokens to avoid re-computation, yet it demands substantial storage space, especially for long sequences. Adaptive KV cache compre…
MiniCache: KV Cache Compression in Depth Dimension for Large Language Models
Akide Liu, Jing Liu, Zizheng Pan +3
A critical approach for efficiently deploying computationally demanding large language models (LLMs) is Key-Value (KV) caching. The KV cache stores key-value states of previously g…
DragAnything: Motion Control for Anything using Entity Representation
Weijia Wu, Zhuang Li, Yuchao Gu +7
We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation. Comparison to existing motion control m…
Towards Accurate Post-training Quantization for Reparameterized Models
Luoming Zhang, Yefei He, Wen Fei +4
Model reparameterization is a widely accepted technique for improving inference speed without compromising performance. However, current Post-training Quantization (PTQ) methods of…
DSText V2: A Comprehensive Video Text Spotting Dataset for Dense and Small Text
Weijia Wu, Yiming Zhang, Yefei He +4
Recently, video text detection, tracking, and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchm…
Paragraph-to-Image Generation with Information-Enriched Diffusion Model
Weijia Wu, Zhuang Li, Yefei He +6
Text-to-image (T2I) models have recently experienced rapid development, achieving astonishing performance in terms of fidelity and textual alignment capabilities. However, given a…
Dual Grained Quantization: Efficient Fine-Grained Quantization for LLM
Luoming Zhang, Wen Fei, Weijia Wu +3
Large Language Models (LLMs) pose significant hardware challenges related to memory requirements and computational ability. There are two mainstream quantization schemes for LLMs:…
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models
Yefei He, Jing Liu, Weijia Wu +2
Diffusion models have demonstrated remarkable capabilities in image synthesis and related generative tasks. Nevertheless, their practicality for real-world applications is constrai…