1 citations · 2 across the 11 of their papers we have counts for
12 papers
Saliency-Aware Model Merging
Jungin Park, Jiyoung Lee, Kwanghoon Sohn
Model merging aims to consolidate multiple task-specific models fine-tuned on different datasets into a unified architecture that performs cross-domain proficiency. Current data-fr…
V-LynX: Token Interface Alignment for Video+X LLMs
Jungin Park, Jiyoung Lee, Kwanghoon Sohn
This study introduces an intriguing phenomenon in Video LLMs: rather than merely translating frames into textual embeddings, Video LLMs establish a continuous manifold, token inter…
Language-guided Recursive Spatiotemporal Graph Modeling for Video Summarization
Jungin Park, Jiyoung Lee, Kwanghoon Sohn
Video summarization aims to select keyframes that are visually diverse and can represent the whole story of a given video. Previous approaches have focused on global interlinkabili…
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…
Descriptive Image-Text Matching with Graded Contextual Similarity
Jinhyun Jang, Jiyoung Lee, Kwanghoon Sohn
Image-text matching aims to build correspondences between visual and textual data by learning their pairwise similarities. Most existing approaches have adopted sparse binary super…
Bootstrap Your Own Views: Masked Ego-Exo Modeling for Fine-grained View-invariant Video Representations
Jungin Park, Jiyoung Lee, Kwanghoon Sohn
View-invariant representation learning from egocentric (first-person, ego) and exocentric (third-person, exo) videos is a promising approach toward generalizing video understanding…
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…
Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive Inference
Seongheon Park, Hyuk Kwon, Kwanghoon Sohn +1
Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world scenarios by taking account of novel categories in unlabeled datasets. Despi…
Bridging Vision and Language Spaces with Assignment Prediction
Jungin Park, Jiyoung Lee, Kwanghoon Sohn
This paper introduces VLAP, a novel approach that bridges pretrained vision models and large language models (LLMs) to make frozen LLMs understand the visual world. VLAP transforms…
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…
Layer-wise Auto-Weighting for Non-Stationary Test-Time Adaptation
Junyoung Park, Jin Kim, Hyeongjun Kwon +2
Given the inevitability of domain shifts during inference in real-world applications, test-time adaptation (TTA) is essential for model adaptation after deployment. However, the re…