3 papers
cs.CV2026
Follow the Saliency: Supervised Saliency for Retrieval-augmented Dense Video Captioning
Seung hee Choi, MinJu Jeon, Hyunwoo Oh +2
Existing retrieval-augmented approaches for Dense Video Captioning (DVC) often fail to achieve accurate temporal segmentation aligned with true event boundaries, as they rely on he…
cs.LG2025
Contextually Guided Transformers via Low-Rank Adaptation
Andrey Zhmoginov, Jihwan Lee, Max Vladymyrov +1
Large Language Models (LLMs) based on Transformers excel at text processing, but their reliance on prompts for specialized behavior introduces computational overhead. We propose a…
cs.LG2025
Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones
Andrey Zhmoginov, Jihwan Lee, Mark Sandler
Modern Foundation Models (FMs) are typically trained on corpora spanning a wide range of different data modalities, topics and downstream tasks. Utilizing these models can be very…