6 papers · 1 filter
Intrinsic Temporal Adaptation of CLIP for Partially Relevant Video Retrieval
Hyun Seok Seong, Woojin Jun, SuBeen Lee +1
Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos that contain moments relevant to a text query. Since the target moment occupies only a portion of the vi…
Selective Synergistic Learning for Video Object-Centric Learning
WonJun Moon, Jae-Pil Heo
Typical video object-centric learning (VOCL) approaches employ slot-based frameworks that rely on reconstruction-driven encoder-decoder architectures, where learning is mediated by…
Temporally Consistent Long-Term Memory for 3D Single Object Tracking
Jaejoon Yoo, SuBeen Lee, Yerim Jeon +2
3D Single Object Tracking (3D-SOT) aims to localize a target object across a sequence of LiDAR point clouds, given its 3D bounding box in the first frame. Recent methods have adopt…
Looking Beyond the Window: Global-Local Aligned CLIP for Training-free Open-Vocabulary Semantic Segmentation
ByeongCheol Lee, Hyun Seok Seong, Sangeek Hyun +3
A sliding-window inference strategy is commonly adopted in recent training-free open-vocabulary semantic segmentation methods to overcome limitation of the CLIP in processing high-…
Reconstruction-Guided Slot Curriculum: Addressing Object Over-Fragmentation in Video Object-Centric Learning
WonJun Moon, Hyun Seok Seong, Jae-Pil Heo
Video Object-Centric Learning seeks to decompose raw videos into a small set of object slots, but existing slot-attention models often suffer from severe over-fragmentation. This i…
From Vicious to Virtuous Cycles: Synergistic Representation Learning for Unsupervised Video Object-Centric Learning
Hyun Seok Seong, WonJun Moon, Jae-Pil Heo
Unsupervised object-centric learning models, particularly slot-based architectures, have shown great promise in decomposing complex scenes. However, their reliance on reconstructio…