REVIEW 4 cited by
VTimeLLM: Empower LLM to Grasp Video Moments
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Large language models (LLMs) have shown remarkable text understanding capabilities, which have been extended as Video LLMs to handle video data for comprehending visual details. However, existing Video LLMs can only provide a coarse description of the entire video, failing to capture the precise start and end time boundary of specific events. In this paper, we solve this issue via proposing VTimeLLM, a novel Video LLM designed for fine-grained video moment understanding and reasoning with respect to time boundary. Specifically, our VTimeLLM adopts a boundary-aware three-stage training strategy, which respectively utilizes image-text pairs for feature alignment, multiple-event videos to increase temporal-boundary awareness, and high-quality video-instruction tuning to further improve temporal understanding ability as well as align with human intents. Extensive experiments demonstrate that in fine-grained time-related comprehension tasks for videos such as Temporal Video Grounding and Dense Video Captioning, VTimeLLM significantly outperforms existing Video LLMs. Besides, benefits from the fine-grained temporal understanding of the videos further enable VTimeLLM to beat existing Video LLMs in video dialogue benchmark, showing its superior cross-modal understanding and reasoning abilities.
Forward citations
Cited by 4 Pith papers
-
TimePLE: Rethinking Temporal Representation for Video Temporal Grounding
TimePLE predicts a whole video interval as a joint distribution over a position-duration square, rather than predicting start and end separately, and reports higher mIoU across four VTG benchmarks.
-
VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding
An open 4B video MLLM with inflated-3D ViT tokenization and adaptive streaming perception outperforms comparable open models on general, long-video, and streaming benchmarks while using fewer visual tokens.
-
PDB-Eval: An Evaluation of Large Multimodal Models for Description and Explanation of Personalized Driving Behavior
Introduces PDB-Eval, a dual-view benchmark for fine-grained driver behavior description and explanation, and shows fine-tuning on it boosts performance on driving QA and downstream intention and recognition tasks.
-
VideoRoPE: What Makes for Good Video Rotary Position Embedding?
VideoRoPE improves video rotary position embedding by allocating low-frequency channels to time, interleaving spatial channels, and adding tunable temporal spacing, beating prior RoPE variants on long-video benchmarks.
Discussion (0). Continue with ORCID to comment.