6 papers
Seen-to-Scene: Keep the Seen, Generate the Unseen for Video Outpainting
Inseok Jeon, Minhyeok Lee, Seunghoon Lee +3
Video outpainting aims to expand the visible content of a video beyond the original frame boundaries while preserving spatial fidelity and temporal coherence across frames. Existin…
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…
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…
SwiftVGGT: A Scalable Visual Geometry Grounded Transformer for Large-Scale Scenes
Jungho Lee, Minhyeok Lee, Sunghun Yang +2
3D reconstruction in large-scale scenes is a fundamental task in 3D perception, but the inherent trade-off between accuracy and computational efficiency remains a significant chall…
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…
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…