3 citations · 5 across the 4 of their papers we have counts for
6 papers
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…
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…
Feature Disentanglement Learning with Switching and Aggregation for Video-based Person Re-Identification
Minjung Kim, MyeongAh Cho, Sangyoun Lee
In video person re-identification (Re-ID), the network must consistently extract features of the target person from successive frames. Existing methods tend to focus only on how to…
Tackling Background Distraction in Video Object Segmentation
Suhwan Cho, Heansung Lee, Minhyeok Lee +4
Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos. One of the main challenges in this task is the existence of background d…
VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation
Su Ho Han, Sukjun Hwang, Seoung Wug Oh +4
For online video instance segmentation (VIS), fully utilizing the information from previous frames in an efficient manner is essential for real-time applications. Most previous met…