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Found 4 papers, 4 papers with code
HawkEye: Training Video-Text LLMs for Grounding Text in Videos
Video-text Large Language Models (video-text LLMs) have shown remarkable performance in answering questions and holding conversations on simple videos.
Ranked #4 on
Video Question Answering
on MVBench
LSTP: Language-guided Spatial-Temporal Prompt Learning for Long-form Video-Text Understanding
Despite progress in video-language modeling, the computational challenge of interpreting long-form videos in response to task-specific linguistic queries persists, largely due to the complexity of high-dimensional video data and the misalignment between language and visual cues over space and time.
STAIR: Spatial-Temporal Reasoning with Auditable Intermediate Results for Video Question Answering
However, most models can only handle simple videos in terms of temporal reasoning, and their performance tends to drop when answering temporal-reasoning questions on long and informative videos.
VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions
Video-grounded dialogue understanding is a challenging problem that requires machine to perceive, parse and reason over situated semantics extracted from weakly aligned video and dialogues.
