109 citations · 416 across the 34 of their papers we have counts for
36 papers
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark
Sanghyun Woo, Kwanyong Park, Inkyu Shin +2
Multi-target multi-camera tracking is a crucial task that involves identifying and tracking individuals over time using video streams from multiple cameras. This task has practical…
Long-range Multimodal Pretraining for Movie Understanding
Dawit Mureja Argaw, Joon-Young Lee, Markus Woodson +2
Learning computer vision models from (and for) movies has a long-standing history. While great progress has been attained, there is still a need for a pretrained multimodal model t…
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples
Chenshuang Zhang, Chaoning Zhang, Taegoo Kang +3
Segment Anything Model (SAM) has attracted significant attention recently, due to its impressive performance on various downstream tasks in a zero-short manner. Computer vision (CV…
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
Chaoning Zhang, Chenshuang Zhang, Chenghao Li +13
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence…
A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI
Chenshuang Zhang, Chaoning Zhang, Sheng Zheng +4
Generative AI has demonstrated impressive performance in various fields, among which speech synthesis is an interesting direction. With the diffusion model as the most popular gene…
Hindi as a Second Language: Improving Visually Grounded Speech with Semantically Similar Samples
Hyeonggon Ryu, Arda Senocak, In So Kweon +1
The objective of this work is to explore the learning of visually grounded speech models (VGS) from multilingual perspective. Bilingual VGS models are generally trained with an equ…
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
Taeyeop Lee, Jonathan Tremblay, Valts Blukis +6
Test-time adaptation methods have been gaining attention recently as a practical solution for addressing source-to-target domain gaps by gradually updating the model without requir…
Self-Sufficient Framework for Continuous Sign Language Recognition
Youngjoon Jang, Youngtaek Oh, Jae Won Cho +4
The goal of this work is to develop self-sufficient framework for Continuous Sign Language Recognition (CSLR) that addresses key issues of sign language recognition. These include…
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
Chaoning Zhang, Chenshuang Zhang, Sheng Zheng +14
As ChatGPT goes viral, generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. With su…
Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation
Daehan Kim, Minseok Seo, Kwanyong Park +4
Mixup provides interpolated training samples and allows the model to obtain smoother decision boundaries for better generalization. The idea can be naturally applied to the domain…
Complementary Random Masking for RGB-Thermal Semantic Segmentation
Ukcheol Shin, Kyunghyun Lee, In So Kweon +1
RGB-thermal semantic segmentation is one potential solution to achieve reliable semantic scene understanding in adverse weather and lighting conditions. However, the previous studi…
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Axi Niu, Kang Zhang, Trung X. Pham +4
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resol…
Semi-Supervised Image Captioning by Adversarially Propagating Labeled Data
Dong-Jin Kim, Tae-Hyun Oh, Jinsoo Choi +1
We present a novel data-efficient semi-supervised framework to improve the generalization of image captioning models. Constructing a large-scale labeled image captioning dataset is…
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
Sanghyun Woo, Shoubhik Debnath, Ronghang Hu +4
Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2…
Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation
Sungsu Hur, Inkyu Shin, Kwanyong Park +2
Universal Domain Adaptation aims to transfer the knowledge between the datasets by handling two shifts: domain-shift and category-shift. The main challenge is correctly distinguish…
Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management
Junha Song, Kwanyong Park, InKyu Shin +3
Prior to the deployment of robotic systems, pre-training the deep-recognition models on all potential visual cases is infeasible in practice. Hence, test-time adaptation (TTA) allo…
MATE: Masked Autoencoders are Online 3D Test-Time Learners
M. Jehanzeb Mirza, Inkyu Shin, Wei Lin +8
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring…
Signing Outside the Studio: Benchmarking Background Robustness for Continuous Sign Language Recognition
Youngjoon Jang, Youngtaek Oh, Jae Won Cho +3
The goal of this work is background-robust continuous sign language recognition. Most existing Continuous Sign Language Recognition (CSLR) benchmarks have fixed backgrounds and are…
Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging
Joohyung Lee, Jieun Oh, Inkyu Shin +4
Volumetric images from Magnetic Resonance Imaging (MRI) provide invaluable information in preoperative staging of rectal cancer. Above all, accurate preoperative discrimination bet…
Per-Clip Video Object Segmentation
Kwanyong Park, Sanghyun Woo, Seoung Wug Oh +2
Recently, memory-based approaches show promising results on semi-supervised video object segmentation. These methods predict object masks frame-by-frame with the help of frequently…
Generative Bias for Robust Visual Question Answering
Jae Won Cho, Dong-jin Kim, Hyeonggon Ryu +1
The task of Visual Question Answering (VQA) is known to be plagued by the issue of VQA models exploiting biases within the dataset to make its final prediction. Various previous en…
The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing
Dawit Mureja Argaw, Fabian Caba Heilbron, Joon-Young Lee +2
Machine learning is transforming the video editing industry. Recent advances in computer vision have leveled-up video editing tasks such as intelligent reframing, rotoscoping, colo…
ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation
Fei Pan, Sungsu Hur, Seokju Lee +2
Open compound domain adaptation (OCDA) considers the target domain as the compound of multiple unknown homogeneous subdomains. The goal of OCDA is to minimize the domain gap betwee…
TubeFormer-DeepLab: Video Mask Transformer
Dahun Kim, Jun Xie, Huiyu Wang +6
We present TubeFormer-DeepLab, the first attempt to tackle multiple core video segmentation tasks in a unified manner. Different video segmentation tasks (e.g., video semantic/inst…