6 papers · 1 filter
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…
Masking Matters: Unlocking the Spatial Reasoning Capabilities of LLMs for 3D Scene-Language Understanding
Yerim Jeon, Miso Lee, WonJun Moon +1
Recent advances in 3D scene-language understanding have leveraged Large Language Models (LLMs) for 3D reasoning by transferring their general reasoning ability to 3D multi-modal co…
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…
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…