6 papers
Rethinking RAG in Long Videos: What to Retrieve and How to Use It?
Yuho Lee, Jisu Shin, Nicole Hee-Yeon Kim +5
Retrieval-augmented generation is extending beyond text to long videos, where query-relevant chunks can be represented across multiple modalities and temporal granularities. Progre…
FLoC: Facility Location-Based Efficient Visual Token Compression for Long Video Understanding
Janghoon Cho, Jungsoo Lee, Munawar Hayat +3
Recent studies in long video understanding have harnessed the advanced visual-language reasoning capabilities of Large Multimodal Models (LMMs), driving the evolution of video-LMMs…
Generalized Contrastive Learning for Universal Multimodal Retrieval
Jungsoo Lee, Janghoon Cho, Hyojin Park +4
Despite their consistent performance improvements, cross-modal retrieval models (e.g., CLIP) show degraded performances with retrieving keys composed of fused image-text modality (…
Personalized OVSS: Understanding Personal Concept in Open-Vocabulary Semantic Segmentation
Sunghyun Park, Jungsoo Lee, Shubhankar Borse +4
While open-vocabulary semantic segmentation (OVSS) can segment an image into semantic regions based on arbitrarily given text descriptions even for classes unseen during training,…
CustomKD: Customizing Large Vision Foundation for Edge Model Improvement via Knowledge Distillation
Jungsoo Lee, Debasmit Das, Munawar Hayat +3
We propose a novel knowledge distillation approach, CustomKD, that effectively leverages large vision foundation models (LVFMs) to enhance the performance of edge models (e.g., Mob…
CA-LoRA: Concept-Aware LoRA for Domain-Aligned Segmentation Dataset Generation
Minho Park, Sunghyun Park, Jungsoo Lee +5
This paper addresses the challenge of data scarcity in semantic segmentation by generating datasets through text-to-image (T2I) generation models, reducing image acquisition and la…