5 citations · 6 across the 4 of their papers we have counts for
5 papers
SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability
Jiankang Wang, Zhihan Zhang, Zhihang Liu +4
Multimodal large language models (MLLMs) have made remarkable progress in either temporal or spatial localization. However, they struggle to perform spatio-temporal video grounding…
CAPability: A Comprehensive Visual Caption Benchmark for Evaluating Both Correctness and Thoroughness
Zhihang Liu, Chen-Wei Xie, Bin Wen +9
Visual captioning benchmarks have become outdated with the emergence of modern multimodal large language models (MLLMs), as the brief ground-truth sentences and traditional metrics…
Hallucination Mitigation Prompts Long-term Video Understanding
Yiwei Sun, Zhihang Liu, Chuanbin Liu +3
Recently, multimodal large language models have made significant advancements in video understanding tasks. However, their ability to understand unprocessed long videos is very lim…
AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation
Jiannan Ge, Lingxi Xie, Hongtao Xie +4
A serious issue that harms the performance of zero-shot visual recognition is named objective misalignment, i.e., the learning objective prioritizes improving the recognition accur…
Towards Balanced Alignment: Modal-Enhanced Semantic Modeling for Video Moment Retrieval
Zhihang Liu, Jun Li, Hongtao Xie +4
Video Moment Retrieval (VMR) aims to retrieve temporal segments in untrimmed videos corresponding to a given language query by constructing cross-modal alignment strategies. Howeve…