22 citations · 22 across the 5 of their papers we have counts for
6 papers
Learning What To Hear: Boosting Sound-Source Association For Robust Audiovisual Instance Segmentation
Jinbae Seo, Hyeongjun Kwon, Kwonyoung Kim +2
Audiovisual instance segmentation (AVIS) requires accurately localizing and tracking sounding objects throughout video sequences. Existing methods suffer from visual bias stemming…
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…
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…
TemporalMaxer: Maximize Temporal Context with only Max Pooling for Temporal Action Localization
Tuan N. Tang, Kwonyoung Kim, Kwanghoon Sohn
Temporal Action Localization (TAL) is a challenging task in video understanding that aims to identify and localize actions within a video sequence. Recent studies have emphasized t…
SimOn: A Simple Framework for Online Temporal Action Localization
Tuan N. Tang, Jungin Park, Kwonyoung Kim +1
Online Temporal Action Localization (On-TAL) aims to immediately provide action instances from untrimmed streaming videos. The model is not allowed to utilize future frames and any…
PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation
Kwonyoung Kim, Jungin Park, Jiyoung Lee +2
Online stereo adaptation tackles the domain shift problem, caused by different environments between synthetic (training) and real (test) datasets, to promptly adapt stereo models i…