5 citations · 7 across the 14 of their papers we have counts for
15 papers
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…
Dissect and Prune: Enhancing Robustness in AI-Generated Image Detection
Dahye Kim, Jaehyun Choi, Hyun Seok Seong +4
While existing AI-generated image detectors report high performance, we identify that this is largely driven by a critical prediction asymmetry: a bias toward the real class that s…
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…
Auxiliary Descriptive Knowledge for Few-Shot Adaptation of Vision-Language Model
SuBeen Lee, GilHan Park, WonJun Moon +2
Despite the impressive zero-shot capabilities of Vision-Language Models (VLMs), they often struggle in downstream tasks with distribution shifts from the pre-training data. Few-Sho…
Selective Contrastive Learning for Weakly Supervised Affordance Grounding
WonJun Moon, Hyun Seok Seong, Jae-Pil Heo
Facilitating an entity's interaction with objects requires accurately identifying parts that afford specific actions. Weakly supervised affordance grounding (WSAG) seeks to imitate…
Temporal Alignment-Free Video Matching for Few-shot Action Recognition
SuBeen Lee, WonJun Moon, Hyun Seok Seong +1
Few-Shot Action Recognition (FSAR) aims to train a model with only a few labeled video instances. A key challenge in FSAR is handling divergent narrative trajectories for precise v…
Foreground-Covering Prototype Generation and Matching for SAM-Aided Few-Shot Segmentation
Suho Park, SuBeen Lee, Hyun Seok Seong +2
We propose Foreground-Covering Prototype Generation and Matching to resolve Few-Shot Segmentation (FSS), which aims to segment target regions in unlabeled query images based on lab…
Progressive Proxy Anchor Propagation for Unsupervised Semantic Segmentation
Hyun Seok Seong, WonJun Moon, SuBeen Lee +1
The labor-intensive labeling for semantic segmentation has spurred the emergence of Unsupervised Semantic Segmentation. Recent studies utilize patch-wise contrastive learning based…
Task-Disruptive Background Suppression for Few-Shot Segmentation
Suho Park, SuBeen Lee, Sangeek Hyun +2
Few-shot segmentation aims to accurately segment novel target objects within query images using only a limited number of annotated support images. The recent works exploit support…
Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification
SuBeen Lee, WonJun Moon, Hyun Seok Seong +1
The difficulty of the fine-grained image classification mainly comes from a shared overall appearance across classes. Thus, recognizing discriminative details, such as eyes and bea…
Leveraging Hidden Positives for Unsupervised Semantic Segmentation
Hyun Seok Seong, WonJun Moon, SuBeen Lee +1
Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer…
Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition
WonJun Moon, Hyun Seok Seong, Jae-Pil Heo
A dramatic increase in real-world video volume with extremely diverse and emerging topics naturally forms a long-tailed video distribution in terms of their categories, and it spot…
Difficulty-Aware Simulator for Open Set Recognition
WonJun Moon, Junho Park, Hyun Seok Seong +2
Open set recognition (OSR) assumes unknown instances appear out of the blue at the inference time. The main challenge of OSR is that the response of models for unknowns is totally…