8 citations · 17 across the 13 of their papers we have counts for
14 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…
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…
MATHENA: Mamba-based Architectural Tooth Hierarchical Estimator and Holistic Evaluation Network for Anatomy
Kyeonghun Kim, Jaehyung Park, Youngung Han +17
Dental diagnosis from Orthopantomograms (OPGs) requires coordination of tooth detection, caries segmentation (CarSeg), anomaly detection (AD), and dental developmental staging (DDS…
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…
White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation
Jiyun Im, SuBeen Lee, Miso Lee +1
Few-shot 3D Point Cloud Semantic Segmentation (FS-PCS) aims to predict per-point labels for an unlabeled point cloud, given only a few labeled examples. To extract representations…
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…
Mutually-Aware Feature Learning for Few-Shot Object Counting
Yerim Jeon, Subeen Lee, Jihwan Kim +1
Few-shot object counting has garnered significant attention for its practicality as it aims to count target objects in a query image based on given exemplars without additional tra…
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…
Mitigating Background Shift in Class-Incremental Semantic Segmentation
Gilhan Park, WonJun Moon, SuBeen Lee +2
Class-Incremental Semantic Segmentation(CISS) aims to learn new classes without forgetting the old ones, using only the labels of the new classes. To achieve this, two popular stra…
Correlation-Guided Query-Dependency Calibration for Video Temporal Grounding
WonJun Moon, Sangeek Hyun, SuBeen Lee +1
Temporal Grounding is to identify specific moments or highlights from a video corresponding to textual descriptions. Typical approaches in temporal grounding treat all video clips…
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…
Task Discrepancy Maximization for Fine-grained Few-Shot Classification
SuBeen Lee, WonJun Moon, Jae-Pil Heo
Recognizing discriminative details such as eyes and beaks is important for distinguishing fine-grained classes since they have similar overall appearances. In this regard, we intro…