5 citations · 13 across the 9 of their papers we have counts for
9 papers
OTT-Vid: Optimal Transport Temporal Token Compression for Video Large Language Models
Minseok Kang, Minhyeok Lee, Jungho Lee +6
As Video Large Language Models (Video-LLMs) scale to longer and more complex videos, their inference cost grows rapidly due to the large volume of visual tokens accumulated across…
An Analysis Focused on Womens Safety: Can VAD Models Be Enhanced by a Multi-modal Dataset?
Sangeeta ., Maddikuntla Sai Prajwal, Debi Prosad Dogra +4
Women's safety and security are paramount for a modern society. Often, crimes scenes get recorded through low-resolution CCTV cameras limiting the efficiency of video anomaly detec…
Effective SAM Combination for Open-Vocabulary Semantic Segmentation
Minhyeok Lee, Suhwan Cho, Jungho Lee +4
Open-vocabulary semantic segmentation aims to assign pixel-level labels to images across an unlimited range of classes. Traditional methods address this by sequentially connecting…
Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation
Minhyeok Lee, Dogyoon Lee, Jungho Lee +4
Referring Image Segmentation (RIS) aims to segment target objects expressed in natural language within a scene at the pixel level. Various recent RIS models have achieved state-of-…
MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation
JunYong Choi, SeokYeong Lee, Haesol Park +3
We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-vi…
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network
Kamalakar Thakare, Yash Raghuwanshi, Debi Prosad Dogra +2
Unsupervised approaches for video anomaly detection may not perform as good as supervised approaches. However, learning unknown types of anomalies using an unsupervised approach is…
Dual Prototype Attention for Unsupervised Video Object Segmentation
Suhwan Cho, Minhyeok Lee, Seunghoon Lee +4
Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos. The primary techniques used in unsupervised VOS are 1) the collaboration…
Probabilistic Representations for Video Contrastive Learning
Jungin Park, Jiyoung Lee, Ig-Jae Kim +1
This paper presents Probabilistic Video Contrastive Learning, a self-supervised representation learning method that bridges contrastive learning with probabilistic representation.…
Video Synopsis Generation Using Spatio-Temporal Groups
A. Ahmed, D. P. Dogra, S. Kar +4
Millions of surveillance cameras operate at 24x7 generating huge amount of visual data for processing. However, retrieval of important activities from such a large data can be time…