36 citations · 134 across the 31 of their papers we have counts for
31 papers
AnthroTAP: Learning Point Tracking with Real-World Motion
Inès Hyeonsu Kim, Seokju Cho, Jahyeok Koo +5
Point tracking models often struggle to generalize to real-world videos because large-scale training data is predominantly synthetic$\unicode{x2014}$the only source currently feasi…
InterRVOS: Interaction-aware Referring Video Object Segmentation
Woojeong Jin, Seongchan Kim, Jaeho Lee +1
Referring video object segmentation (RVOS) aims to segment objects in a video described by a natural language expression. However, most existing approaches focus on segmenting only…
User-friendly Image Editing with Minimal Text Input: Leveraging Captioning and Injection Techniques
Sunwoo Kim, Wooseok Jang, Hyunsu Kim +4
Recent text-driven image editing in diffusion models has shown remarkable success. However, the existing methods assume that the user's description sufficiently grounds the context…
DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation
Jiuhn Song, Seonghoon Park, Honggyu An +4
Neural radiance fields (NeRF) shows powerful performance in novel view synthesis and 3D geometry reconstruction, but it suffers from critical performance degradation when the numbe…
Diffusion Model for Dense Matching
Jisu Nam, Gyuseong Lee, Sunwoo Kim +4
The objective for establishing dense correspondence between paired images consists of two terms: a data term and a prior term. While conventional techniques focused on defining han…
DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation
Susung Hong, Junyoung Seo, Heeseong Shin +2
In the paradigm of AI-generated content (AIGC), there has been increasing attention to transferring knowledge from pre-trained text-to-image (T2I) models to text-to-video (T2V) gen…
Panoramic Image-to-Image Translation
Soohyun Kim, Junho Kim, Taekyung Kim +4
In this paper, we tackle the challenging task of Panoramic Image-to-Image translation (Pano-I2I) for the first time. This task is difficult due to the geometric distortion of panor…
Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation
Junyoung Seo, Wooseok Jang, Min-Seop Kwak +6
Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neura…
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic Models
Gyeongnyeon Kim, Wooseok Jang, Gyuseong Lee +3
Generative models have recently undergone significant advancement due to the diffusion models. The success of these models can be often attributed to their use of guidance techniqu…
You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona
Jungwoo Lim, Myunghoon Kang, Yuna Hur +8
To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model th…
GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency
Min-seop Kwak, Jiuhn Song, Seungryong Kim
We present a novel framework to regularize Neural Radiance Field (NeRF) in a few-shot setting with a geometry-aware consistency regularization. The proposed approach leverages a re…
DiffFace: Diffusion-based Face Swapping with Facial Guidance
Kihong Kim, Yunho Kim, Seokju Cho +5
In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, an…
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels
Daehwan Kim, Kwangrok Ryoo, Hansang Cho +1
Annotating the dataset with high-quality labels is crucial for performance of deep network, but in real world scenarios, the labels are often contaminated by noise. To address this…
MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth Estimation
Jongbeom Baek, Gyeongnyeon Kim, Seonghoon Park +3
We propose MaskingDepth, a novel semi-supervised learning framework for monocular depth estimation to mitigate the reliance on large ground-truth depth quantities. MaskingDepth is…
Controllable Style Transfer via Test-time Training of Implicit Neural Representation
Sunwoo Kim, Youngjo Min, Younghun Jung +1
We propose a controllable style transfer framework based on Implicit Neural Representation that pixel-wisely controls the stylized output via test-time training. Unlike traditional…
3D GAN Inversion with Pose Optimization
Jaehoon Ko, Kyusun Cho, Daewon Choi +2
With the recent advances in NeRF-based 3D aware GANs quality, projecting an image into the latent space of these 3D-aware GANs has a natural advantage over 2D GAN inversion: not on…
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
Sunghwan Hong, Jisu Nam, Seokju Cho +4
Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters. Th…
Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling
Yunsung Lee, Gyuseong Lee, Kwangrok Ryoo +3
There are two de facto standard architectures in recent computer vision: Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Strong inductive biases of convolution…
Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
Susung Hong, Gyuseong Lee, Wooseok Jang +1
Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely attributed to the use of class- or text-c…
Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence
Sunghwan Hong, Seokju Cho, Seungryong Kim +1
We present a novel architecture for dense correspondence. The current state-of-the-art are Transformer-based approaches that focus on either feature descriptors or cost volume aggr…
ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization
Jiwon Kim, Youngjo Min, Daehwan Kim +4
We present a novel semi-supervised learning framework that intelligently leverages the consistency regularization between the model's predictions from two strongly-augmented views…
MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image Translation
Junyoung Seo, Gyuseong Lee, Seokju Cho +2
We present a novel method for exemplar-based image translation, called matching interleaved diffusion models (MIDMs). Most existing methods for this task were formulated as GAN-bas…
LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data
Jihye Park, Sunwoo Kim, Soohyun Kim +4
Existing techniques for image-to-image translation commonly have suffered from two critical problems: heavy reliance on per-sample domain annotation and/or inability of handling mu…
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation
Sunghwan Hong, Seokju Cho, Jisu Nam +2
This paper presents a novel cost aggregation network, called Volumetric Aggregation with Transformers (VAT), for few-shot segmentation. The use of transformers can benefit correlat…