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cs.CV2026
Which Tokens to Merge? Diffusion Dynamics for Efficient Image Generation
SeungJu Cha, Ye-Chan Kim, HyunGee Kim +2
Token merging has emerged as an effective training-free strategy for accelerating diffusion models by reducing redundant token computation while minimizing generation degradation.…
cs.CV2026
Seeing Before Synthesizing: VLM-Guided Transition Event Discovery for Weakly-Supervised Dense Video Captioning
Ye-Chan Kim, Seunghee Choi, SeungJu Cha +4
Weakly-Supervised Dense Video Captioning aims to localize and describe multiple events in untrimmed videos given only an ordered set of event-level captions per video. Recent work…
cs.CV2026
SAIL: Similarity-Aware Guidance and Inter-Caption Augmentation-based Learning for Weakly-Supervised Dense Video Captioning
Ye-Chan Kim, SeungJu Cha, Si-Woo Kim +3
Weakly-Supervised Dense Video Captioning aims to localize and describe events in videos trained only on caption annotations, without temporal boundaries. Prior work introduced an i…