With the rapid expansion of online video content, video temporal grounding has become essential for localizing moments based on user-specific queries, comprising two subtasks: moment retrieval (MR) and highlight detection (HD). While recent approaches adopt DETR-style decoders or temporal pyramid architectures, they re...
Sanghyeok Lee, Juyeon Ko, Joonmyung Choi et al.· 0 citations
WearableQA, a benchmark comprising 4,084 10-option multiple-choice questions constructed from the wearable time series, blood biomarkers, and demographics of 200 real users, provides a realistic and diagnostic benchmark for evaluating LLM reasoning over real-world wearable data.
Ji Soo Lee, Xi-Lun Chen, Pierce Chuang et al.· 0 citations
Understanding long-form video remains a fundamental challenge for multimodal large language models (MLLMs). Sparse frame sampling fails to capture fine-grained visual details, while dense sampling quickly exceeds context length limits. Retrieval-augmented generation (RAG) offers a promising middle ground by selectively...
Dong-Hee Kim, Seonwoo Choi, Changbeen Kim et al.· 1 citation· ⚡1
Context-Calibrated DPO (C$^2$-DPO), which directly maximizes CPG while preserving the original preference ordering, is proposed, which substantially reduces hallucination without compromising general reasoning.
Byungoh Ko, Jinyoung Park, Jongha Kim et al.· 0 citations
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