21 citations · 43 across the 17 of their papers we have counts for
17 papers
ForeSea: AI Forensic Search with Multi-modal Queries for Video Surveillance
Hyojin Park, Yi Li, Janghoon Cho +8
Despite decades of work, surveillance still struggles in searching and reasoning about specific targets across long, multi-camera videos. Existing methods - tracking, retrieval, an…
FLoC: Facility Location-Based Efficient Visual Token Compression for Long Video Understanding
Janghoon Cho, Jungsoo Lee, Munawar Hayat +3
Recent studies in long video understanding have harnessed the advanced visual-language reasoning capabilities of Large Multimodal Models (LMMs), driving the evolution of video-LMMs…
Generalized Contrastive Learning for Universal Multimodal Retrieval
Jungsoo Lee, Janghoon Cho, Hyojin Park +4
Despite their consistent performance improvements, cross-modal retrieval models (e.g., CLIP) show degraded performances with retrieving keys composed of fused image-text modality (…
Personalized OVSS: Understanding Personal Concept in Open-Vocabulary Semantic Segmentation
Sunghyun Park, Jungsoo Lee, Shubhankar Borse +4
While open-vocabulary semantic segmentation (OVSS) can segment an image into semantic regions based on arbitrarily given text descriptions even for classes unseen during training,…
CustomKD: Customizing Large Vision Foundation for Edge Model Improvement via Knowledge Distillation
Jungsoo Lee, Debasmit Das, Munawar Hayat +3
We propose a novel knowledge distillation approach, CustomKD, that effectively leverages large vision foundation models (LVFMs) to enhance the performance of edge models (e.g., Mob…
CA-LoRA: Concept-Aware LoRA for Domain-Aligned Segmentation Dataset Generation
Minho Park, Sunghyun Park, Jungsoo Lee +5
This paper addresses the challenge of data scarcity in semantic segmentation by generating datasets through text-to-image (T2I) generation models, reducing image acquisition and la…
Multi-Context Temporal Consistent Modeling for Referring Video Object Segmentation
Sun-Hyuk Choi, Hayoung Jo, Seong-Whan Lee
Referring video object segmentation aims to segment objects within a video corresponding to a given text description. Existing transformer-based temporal modeling approaches face c…
Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimization
Jungsoo Lee, Debasmit Das, Jaegul Choo +1
Test-time adaptation (TTA) methods, which generally rely on the model's predictions (e.g., entropy minimization) to adapt the source pretrained model to the unlabeled target domain…
Progressive Random Convolutions for Single Domain Generalization
Seokeon Choi, Debasmit Das, Sungha Choi +3
Single domain generalization aims to train a generalizable model with only one source domain to perform well on arbitrary unseen target domains. Image augmentation based on Random…
EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization
Junha Song, Jungsoo Lee, In So Kweon +1
This paper presents a simple yet effective approach that improves continual test-time adaptation (TTA) in a memory-efficient manner. TTA may primarily be conducted on edge devices…
TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation
Hyesu Lim, Byeonggeun Kim, Jaegul Choo +1
This paper proposes a novel batch normalization strategy for test-time adaptation. Recent test-time adaptation methods heavily rely on the modified batch normalization, i.e., trans…
Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes
Sungha Choi, Seunghan Yang, Seokeon Choi +1
This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained on the source domain using only unlabeled online data from the target domain to allevia…
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
Sanghun Jung, Jungsoo Lee, Daehoon Gwak +2
Identifying unexpected objects on roads in semantic segmentation (e.g., identifying dogs on roads) is crucial in safety-critical applications. Existing approaches use images of une…
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening
Sungha Choi, Sanghun Jung, Huiwon Yun +3
Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving. To addre…
Towards Lightweight Lane Detection by Optimizing Spatial Embedding
Seokwoo Jung, Sungha Choi, Mohammad Azam Khan +1
A number of lane detection methods depend on a proposal-free instance segmentation because of its adaptability to flexible object shape, occlusion, and real-time application. This…
Cars Can't Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks
Sungha Choi, Joanne T. Kim, Jaegul Choo
This paper exploits the intrinsic features of urban-scene images and proposes a general add-on module, called height-driven attention networks (HANet), for improving semantic segme…
Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation
Wonwoong Cho, Sungha Choi, David Keetae Park +2
Recently, unsupervised exemplar-based image-to-image translation, conditioned on a given exemplar without the paired data, has accomplished substantial advancements. In order to tr…