13 citations · 31 across the 15 of their papers we have counts for
6 papers · 1 filter
VEGA: Learning Interleaved Image-Text Comprehension in Vision-Language Large Models
Chenyu Zhou, Mengdan Zhang, Peixian Chen +5
The swift progress of Multi-modal Large Models (MLLMs) has showcased their impressive ability to tackle tasks blending vision and language. Yet, most current models and benchmarks…
Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis
Chaoyou Fu, Yuhan Dai, Yongdong Luo +18
In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements. However, the predominant focus rem…
Cantor: Inspiring Multimodal Chain-of-Thought of MLLM
Timin Gao, Peixian Chen, Mengdan Zhang +8
With the advent of large language models(LLMs) enhanced by the chain-of-thought(CoT) methodology, visual reasoning problem is usually decomposed into manageable sub-tasks and tackl…
A General and Efficient Training for Transformer via Token Expansion
Wenxuan Huang, Yunhang Shen, Jiao Xie +5
The remarkable performance of Vision Transformers (ViTs) typically requires an extremely large training cost. Existing methods have attempted to accelerate the training of ViTs, ye…
RESTORE: Towards Feature Shift for Vision-Language Prompt Learning
Yuncheng Yang, Chuyan Zhang, Zuopeng Yang +6
Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks. However, the prompts that are independently opt…
Sinkhorn Distance Minimization for Knowledge Distillation
Xiao Cui, Yulei Qin, Yuting Gao +7
Knowledge distillation (KD) has been widely adopted to compress large language models (LLMs). Existing KD methods investigate various divergence measures including the Kullback-Lei…