8 citations · 11 across the 8 of their papers we have counts for
12 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…
CMTM: Cross-Modal Token Modulation for Unsupervised Video Object Segmentation
Inseok Jeon, Suhwan Cho, Minhyeok Lee +6
Recent advances in unsupervised video object segmentation have highlighted the potential of two-stream architectures that integrate appearance and motion cues. However, fully lever…
MoRGS: Efficient Per-Gaussian Motion Reasoning for Streamable Dynamic 3D Scenes
Wonjoon Lee, Sungmin Woo, Donghyeong Kim +3
Online reconstruction of dynamic scenes aims to learn from streaming multi-view inputs under low-latency constraints. The fast training and real-time rendering capabilities of 3D G…
Revisiting Weakly-Supervised Video Scene Graph Generation via Pair Affinity Learning
Minseok Kang, Minhyeok Lee, Minjung Kim +5
Weakly-supervised video scene graph generation (WS-VSGG) aims to parse video content into structured relational triplets without bounding box annotations and with only sparse tempo…
Empower Words: DualGround for Structured Phrase and Sentence-Level Temporal Grounding
Minseok Kang, Minhyeok Lee, Minjung Kim +2
Video Temporal Grounding (VTG) aims to localize temporal segments in long, untrimmed videos that align with a given natural language query. This task typically comprises two subtas…
DepthFlow: Exploiting Depth-Flow Structural Correlations for Unsupervised Video Object Segmentation
Suhwan Cho, Minhyeok Lee, Jungho Lee +2
Unsupervised video object segmentation (VOS) aims to detect the most prominent object in a video. Recently, two-stream approaches that leverage both RGB images and optical flow hav…
GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection
Donghyeong Kim, Chaewon Park, Suhwan Cho +4
Zero-shot anomaly detection (ZSAD) aims to identify anomalies in unseen categories by leveraging CLIP's zero-shot capabilities to match text prompts with visual features. A key cha…
CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images
Jungho Lee, Donghyeong Kim, Dogyoon Lee +6
3D Gaussian Splatting (3DGS) has gained significant attention due to its high-quality novel view rendering, motivating research to address real-world challenges. A critical issue i…
Improving Unsupervised Video Object Segmentation via Fake Flow Generation
Suhwan Cho, Minhyeok Lee, Jungho Lee +4
Unsupervised video object segmentation (VOS), also known as video salient object detection, aims to detect the most prominent object in a video at the pixel level. Recently, two-st…
Sparse-DeRF: Deblurred Neural Radiance Fields from Sparse View
Dogyoon Lee, Donghyeong Kim, Jungho Lee +3
Recent studies construct deblurred neural radiance fields~(DeRF) using dozens of blurry images, which are not practical scenarios if only a limited number of blurry images are avai…
CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion-Blurred Images
Jungho Lee, Donghyeong Kim, Dogyoon Lee +3
3D Gaussian Splatting (3DGS) has gained significant attention for their high-quality novel view rendering, motivating research to address real-world challenges. A critical issue is…
FIMP: Future Interaction Modeling for Multi-Agent Motion Prediction
Sungmin Woo, Minjung Kim, Donghyeong Kim +2
Multi-agent motion prediction is a crucial concern in autonomous driving, yet it remains a challenge owing to the ambiguous intentions of dynamic agents and their intricate interac…