6 citations · 7 across the 2 of their papers we have counts for
5 papers
OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
Pengfei Zhou, Xiaopeng Peng, Jiajun Song +15
Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding and generation tasks. However, generating interleaved image-text content remains a ch…
Efficient High-Resolution Visual Representation Learning with State Space Model for Human Pose Estimation
Hao Zhang, Yongqiang Ma, Wenqi Shao +3
Capturing long-range dependencies while preserving high-resolution visual representations is crucial for dense prediction tasks such as human pose estimation. Vision Transformers (…
Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification
Wenshuo Peng, Kaipeng Zhang, Yue Yang +2
Vision-language foundation models have been incredibly successful in a wide range of downstream computer vision tasks using adaptation methods. However, due to the high cost of obt…
ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Capability for Large Vision-Language Models
Shuo Liu, Kaining Ying, Hao Zhang +8
This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess indivi…
MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
Kaining Ying, Fanqing Meng, Jin Wang +19
Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and embodied navigation. However, existing multimod…