3 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
Faster Parameter-Efficient Tuning with Token Redundancy Reduction
Kwonyoung Kim, Jungin Park, Jin Kim +2
Parameter-efficient tuning (PET) aims to transfer pre-trained foundation models to downstream tasks by learning a small number of parameters. Compared to traditional fine-tuning, w…
Enhancing Source-Free Domain Adaptive Object Detection with Low-confidence Pseudo Label Distillation
Ilhoon Yoon, Hyeongjun Kwon, Jin Kim +3
Source-Free domain adaptive Object Detection (SFOD) is a promising strategy for deploying trained detectors to new, unlabeled domains without accessing source data, addressing sign…
Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping
Hyeongjun Kwon, Jinhyun Jang, Jin Kim +2
Visual scenes are naturally organized in a hierarchy, where a coarse semantic is recursively comprised of several fine details. Exploring such a visual hierarchy is crucial to reco…
Knowing Where to Focus: Event-aware Transformer for Video Grounding
Jinhyun Jang, Jungin Park, Jin Kim +2
Recent DETR-based video grounding models have made the model directly predict moment timestamps without any hand-crafted components, such as a pre-defined proposal or non-maximum s…
Probabilistic Prompt Learning for Dense Prediction
Hyeongjun Kwon, Taeyong Song, Somi Jeong +3
Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations wit…
Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation
Hyeongjun Kwon, Somi Jeong, Sunok Kim +1
We address the problem of few-shot semantic segmentation (FSS), which aims to segment novel class objects in a target image with a few annotated samples. Though recent advances hav…