1 citations · 1 across the 10 of their papers we have counts for
10 papers
A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design
Linhui Xiao, Guiping Cao, Mingyue Guo +6
The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains, yet it has also raised critical concerns regarding computation…
TGV-KV: Text-Grounded KV Eviction for Vision-Language Models
Jizhihui Liu, Ruizi Han, Miao Zhang +4
Vision-Language Models (VLMs) inherit the auto-regressive generation paradigm and cache the keys and values (KV) of all previous tokens to accelerate inference, resulting in memory…
ResidualKV: Residual-Based KV Cache Compression for Efficient Long-Context Inference
Jitai Hao, Qiang Huang, Yaowei Wang +2
Efficient long-context inference faces two coupled bottlenecks: KV-cache memory grows linearly with context length, while attention computation grows quadratically. Existing approa…
A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis
Xianchao Guan, Zhiyuan Fan, Yifeng Wang +11
The development of clinical-grade artificial intelligence in pathology is limited by the scarcity of diverse, high-quality annotated datasets. Generative models offer a potential s…
VideoVista-CulturalLingo: 360 Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension
Xinyu Chen, Yunxin Li, Haoyuan Shi +4
Assessing the video comprehension capabilities of multimodal AI systems can effectively measure their understanding and reasoning abilities. Most video evaluation benchmarks are li…
CountDiffusion: Text-to-Image Synthesis with Training-Free Counting-Guidance Diffusion
Yanyu Li, Pencheng Wan, Liang Han +3
Stable Diffusion has advanced text-to-image synthesis, but training models to generate images with accurate object quantity is still difficult due to the high computational cost an…
A Unified Agentic Framework for Evaluating Conditional Image Generation
Jifang Wang, Xue Yang, Longyue Wang +7
Conditional image generation has gained significant attention for its ability to personalize content. However, the field faces challenges in developing task-agnostic, reliable, and…
Picking the Cream of the Crop: Visual-Centric Data Selection with Collaborative Agents
Zhenyu Liu, Yunxin Li, Baotian Hu +3
To improve Multimodal Large Language Models' (MLLMs) ability to process images and complex instructions, researchers predominantly curate large-scale visual instruction tuning data…
PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models
Jiaqi Zhao, Miao Zhang, Ming Wang +5
Large Language Models (LLMs) suffer severe performance degradation when facing extremely low-bit (sub 2-bit) quantization. Several existing sub 2-bit post-training quantization (PT…
Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis
Jiaqi Zhao, Ming Wang, Miao Zhang +5
Post-training Quantization (PTQ) technique has been extensively adopted for large language models (LLMs) compression owing to its efficiency and low resource requirement. However,…