TraveLER: An Iterative Multi-Agent Framework for Video Question Answering
TraveLER is a modular multi-agent framework that iteratively traverses through a video, locates and extracts relevant information from keyframes through interactive question-answering, evaluates if there is enough information to answer the question, and replans if necessary.
BoViLA: A Novel Self-Training Framework for Video Question Answering Using Large Language Models
BoViLA, a novel self-training framework, leverages the power of large language models (LLMs) to significantly improve video-language alignment and enhance performance in video question answering tasks.
Prompting Video-Language Foundation Models with Domain-Specific Fine-Grained Heuristics for Enhanced Video Question Answering: The HeurVidQA Framework
Integrating domain-specific entity-action heuristics into video-language foundation models significantly improves their performance in Video Question Answering (VideoQA) tasks by enabling more precise and context-aware reasoning.
Multi-Granularity Contrastive Cross-Modal Collaborative Generation Model for End-to-End Long-Term Video Question Answering
This research paper introduces MCG, a novel end-to-end model for VideoQA that leverages multi-granularity contrastive learning and cross-modal collaborative generation to achieve state-of-the-art performance in answering open-ended questions about long-term videos.
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