49 citations · 184 across the 40 of their papers we have counts for
43 papers
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…
Semantic-aware Network for Aerial-to-Ground Image Synthesis
Jinhyun Jang, Taeyong Song, Kwanghoon Sohn
Aerial-to-ground image synthesis is an emerging and challenging problem that aims to synthesize a ground image from an aerial image. Due to the highly different layout and object r…
Hierarchical Visual Primitive Experts for Compositional Zero-Shot Learning
Hanjae Kim, Jiyoung Lee, Seongheon Park +1
Compositional zero-shot learning (CZSL) aims to recognize unseen compositions with prior knowledge of known primitives (attribute and object). Previous works for CZSL often suffer…
PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification
Minsu Kim, Seungryong Kim, JungIn Park +2
Modern data augmentation using a mixture-based technique can regularize the models from overfitting to the training data in various computer vision applications, but a proper data…
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-path Adaptation from Image to Video Transformers
Jungin Park, Jiyoung Lee, Kwanghoon Sohn
In this paper, we efficiently transfer the surpassing representation power of the vision foundation models, such as ViT and Swin, for video understanding with only a few trainable…
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…
Language-free Training for Zero-shot Video Grounding
Dahye Kim, Jungin Park, Jiyoung Lee +2
Given an untrimmed video and a language query depicting a specific temporal moment in the video, video grounding aims to localize the time interval by understanding the text and vi…
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…
Pin the Memory: Learning to Generalize Semantic Segmentation
Jin Kim, Jiyoung Lee, Jungin Park +2
The rise of deep neural networks has led to several breakthroughs for semantic segmentation. In spite of this, a model trained on source domain often fails to work properly in new…
Probabilistic Representations for Video Contrastive Learning
Jungin Park, Jiyoung Lee, Ig-Jae Kim +1
This paper presents Probabilistic Video Contrastive Learning, a self-supervised representation learning method that bridges contrastive learning with probabilistic representation.…
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection
Taeyong Song, Sunok Kim, SungTai Kim +2
Shape deformation of targets in SAR image due to random orientation and partial information loss caused by occlusion of the radar signal, is an essential challenge in SAR ship dete…
Multi-domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolution
Somi Jeong, Jiyoung Lee, Kwanghoon Sohn
Over the past few years, image-to-image (I2I) translation methods have been proposed to translate a given image into diverse outputs. Despite the impressive results, they mainly fo…
Memory-guided Image De-raining Using Time-Lapse Data
Jaehoon Cho, Seungryong Kim, Kwanghoon Sohn
This paper addresses the problem of single image de-raining, that is, the task of recovering clean and rain-free background scenes from a single image obscured by a rainy artifact.…
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…
DIML/CVL RGB-D Dataset: 2M RGB-D Images of Natural Indoor and Outdoor Scenes
Jaehoon Cho, Dongbo Min, Youngjung Kim +1
This manual is intended to provide a detailed description of the DIML/CVL RGB-D dataset. This dataset is comprised of 2M color images and their corresponding depth maps from a grea…
Wide and Narrow: Video Prediction from Context and Motion
Jaehoon Cho, Jiyoung Lee, Changjae Oh +2
Video prediction, forecasting the future frames from a sequence of input frames, is a challenging task since the view changes are influenced by various factors, such as the global…
Self-balanced Learning For Domain Generalization
Jin Kim, Jiyoung Lee, Jungin Park +2
Domain generalization aims to learn a prediction model on multi-domain source data such that the model can generalize to a target domain with unknown statistics. Most existing appr…
Learning Canonical 3D Object Representation for Fine-Grained Recognition
Sunghun Joung, Seungryong Kim, Minsu Kim +2
We propose a novel framework for fine-grained object recognition that learns to recover object variation in 3D space from a single image, trained on an image collection without usi…
CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning
Daniel McDuff, Yale Song, Jiyoung Lee +7
The ability to perform causal and counterfactual reasoning are central properties of human intelligence. Decision-making systems that can perform these types of reasoning have the…
CATs: Cost Aggregation Transformers for Visual Correspondence
Seokju Cho, Sunghwan Hong, Sangryul Jeon +3
We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challeng…
Bridge to Answer: Structure-aware Graph Interaction Network for Video Question Answering
Jungin Park, Jiyoung Lee, Kwanghoon Sohn
This paper presents a novel method, termed Bridge to Answer, to infer correct answers for questions about a given video by leveraging adequate graph interactions of heterogeneous c…
Memory-guided Unsupervised Image-to-image Translation
Somi Jeong, Youngjung Kim, Eungbean Lee +1
We present a novel unsupervised framework for instance-level image-to-image translation. Although recent advances have been made by incorporating additional object annotations, exi…