93 citations · 106 across the 22 of their papers we have counts for
23 papers · 1 filter
Online Versatile Incremental Learning: Towards Class and Domain-Agnostic Adaptation at Any Time
Jae-Ho Lee, Min-Yeong Park, Jun-Yeong Moon +2
Continual learning enables vision systems to adapt to ever-changing data distributions. Despite significant advances, existing approaches fail to capture continuous and concurrent…
Open Your Model's Eyes: Video and Context-Aware Multimodal Backchannel Prediction
Min-Jae Kim, Jun-Yeong Moon, Mujeen Sung +1
Backchannels, which signal listener states like empathy and understanding, are fundamental to natural human interaction. However, current approaches rely solely on audio and text.…
Detecting Unknown Objects via Energy-based Separation for Open World Object Detection
Jun-Woo Heo, Keonhee Park, Gyeong-Moon Park
In this work, we tackle the problem of Open World Object Detection (OWOD). This challenging scenario requires the detector to incrementally learn to classify known objects without…
Disentangled Concepts Speak Louder Than Words: Explainable Video Action Recognition
Jongseo Lee, Wooil Lee, Gyeong-Moon Park +2
Effective explanations of video action recognition models should disentangle how movements unfold over time from the surrounding spatial context. However, existing methods based on…
ConceptSplit: Decoupled Multi-Concept Personalization of Diffusion Models via Token-wise Adaptation and Attention Disentanglement
Habin Lim, Yeongseob Won, Juwon Seo +1
In recent years, multi-concept personalization for text-to-image (T2I) diffusion models to represent several subjects in an image has gained much more attention. The main challenge…
ESSENTIAL: Episodic and Semantic Memory Integration for Video Class-Incremental Learning
Jongseo Lee, Kyungho Bae, Kyle Min +2
In this work, we tackle the problem of video classincremental learning (VCIL). Many existing VCIL methods mitigate catastrophic forgetting by rehearsal training with a few temporal…
Universal Domain Adaptation for Semantic Segmentation
Seun-An Choe, Keon-Hee Park, Jinwoo Choi +1
Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to transfer knowledge from labeled source data to unlabeled target data. However, traditional UDA-SS methods…
PCBEAR: Pose Concept Bottleneck for Explainable Action Recognition
Jongseo Lee, Wooil Lee, Gyeong-Moon Park +2
Human action recognition (HAR) has achieved impressive results with deep learning models, but their decision-making process remains opaque due to their black-box nature. Ensuring i…
ESC: Erasing Space Concept for Knowledge Deletion
Tae-Young Lee, Sundong Park, Minwoo Jeon +2
As concerns regarding privacy in deep learning continue to grow, individuals are increasingly apprehensive about the potential exploitation of their personal knowledge in trained m…
Multispectral Pedestrian Detection with Sparsely Annotated Label
Chan Lee, Seungho Shin, Gyeong-Moon Park +1
Although existing Sparsely Annotated Object Detection (SAOD) approches have made progress in handling sparsely annotated environments in multispectral domain, where only some pedes…
Towards High-fidelity Head Blending with Chroma Keying for Industrial Applications
Hah Min Lew, Sahng-Min Yoo, Hyunwoo Kang +1
We introduce an industrial Head Blending pipeline for the task of seamlessly integrating an actor's head onto a target body in digital content creation. The key challenge stems fro…
Towards Model-Agnostic Dataset Condensation by Heterogeneous Models
Jun-Yeong Moon, Jung Uk Kim, Gyeong-Moon Park
Abstract. The advancement of deep learning has coincided with the proliferation of both models and available data. The surge in dataset sizes and the subsequent surge in computatio…
Versatile Incremental Learning: Towards Class and Domain-Agnostic Incremental Learning
Min-Yeong Park, Jae-Ho Lee, Gyeong-Moon Park
Incremental Learning (IL) aims to accumulate knowledge from sequential input tasks while overcoming catastrophic forgetting. Existing IL methods typically assume that an incoming t…
Online Continuous Generalized Category Discovery
Keon-Hee Park, Hakyung Lee, Kyungwoo Song +1
With the advancement of deep neural networks in computer vision, artificial intelligence (AI) is widely employed in real-world applications. However, AI still faces limitations in…
Open-Set Domain Adaptation for Semantic Segmentation
Seun-An Choe, Ah-Hyung Shin, Keon-Hee Park +2
Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer the pixel-wise knowledge from the labeled source domain to the unlabeled target domain. However, cur…
Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners
Keon-Hee Park, Kyungwoo Song, Gyeong-Moon Park
Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to learn new classes incrementally without forgetting when only a few samples for each class are given.…
GLAD: Global-Local View Alignment and Background Debiasing for Unsupervised Video Domain Adaptation with Large Domain Gap
Hyogun Lee, Kyungho Bae, Seong Jong Ha +3
In this work, we tackle the challenging problem of unsupervised video domain adaptation (UVDA) for action recognition. We specifically focus on scenarios with a substantial domain…
LFS-GAN: Lifelong Few-Shot Image Generation
Juwon Seo, Ji-Su Kang, Gyeong-Moon Park
We address a challenging lifelong few-shot image generation task for the first time. In this situation, a generative model learns a sequence of tasks using only a few samples per t…
RADIO: Reference-Agnostic Dubbing Video Synthesis
Dongyeun Lee, Chaewon Kim, Sangjoon Yu +2
One of the most challenging problems in audio-driven talking head generation is achieving high-fidelity detail while ensuring precise synchronization. Given only a single reference…
Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning
Jun-Yeong Moon, Keon-Hee Park, Jung Uk Kim +1
Continual learning aims to learn a model from a continuous stream of data, but it mainly assumes a fixed number of data and tasks with clear task boundaries. However, in real-world…
LINe: Out-of-Distribution Detection by Leveraging Important Neurons
Yong Hyun Ahn, Gyeong-Moon Park, Seong Tae Kim
It is important to quantify the uncertainty of input samples, especially in mission-critical domains such as autonomous driving and healthcare, where failure predictions on out-of-…
WINE: Wavelet-Guided GAN Inversion and Editing for High-Fidelity Refinement
Chaewon Kim, Seung-Jun Moon, Gyeong-Moon Park
Recent advanced GAN inversion models aim to convey high-fidelity information from original images to generators through methods using generator tuning or high-dimensional feature l…
Continual Unsupervised Domain Adaptation for Semantic Segmentation
Joonhyuk Kim, Sahng-Min Yoo, Gyeong-Moon Park +1
Unsupervised Domain Adaptation (UDA) for semantic segmentation has been favorably applied to real-world scenarios in which pixel-level labels are hard to be obtained. In most of th…