1 citations · 2 across the 25 of their papers we have counts for
32 papers
BLANC: Adaptive Temporal Scheduling for Sign Language Production
Youngmin Kim, Seoyeong Hwang, Sujung Hong +1
Sign language production (SLP) aims to synthesize expressive signing motion from written text. Modeling signing motion presents two key temporal challenges. First, fine-grained han…
On the Necessity of Attention-FFN Split in Vision Transformers
Junhyeok Kim, Jinyeong Kim, Jae Wan Park +1
The standard Transformer architecture relies on a rigid pattern that alternates Attention and Feed-Forward Network (FFN) layers. Despite its widespread adoption, the inductive bias…
ThunderSyncRL: Lossless Acceleration of Agentic Reinforcement Learning
Seil Kang, Hangoo Kang, Tarun Suresh +4
Language models are moving beyond generating answers to pursuing long-horizon goals in interactive environments. Post-training these agents requires long, heterogeneous trajectorie…
Controlling Motion Transfer in Diffusion Transformers via Attention Heads
Sunyoung Jung, Jiwoo Park, Yoonseok Choi +3
Diffusion Transformers (DiTs) have advanced video generation with high-quality, temporally coherent results. However, extending them to motion transfer, which requires following re…
QWERTY: Training-Free Motion Control via Query-Warped Video Diffusion Transformers
Kyobin Choo, Youngmin Kim, Hyunkyung Han +4
Video diffusion transformers (DiTs) generate high-fidelity and temporally coherent videos, yet motion control remains implicit, primarily relying on text prompts. As a result, achi…
Mitigating Mask Prior Drift and Positional Attention Collapse in Large Diffusion Vision-Language Models
Sujung Hong, Chanyong Yoon, Seong Jae Hwang
Large diffusion vision-language models (LDVLMs) have recently emerged as a promising alternative to autoregressive models, enabling parallel decoding for efficient inference and le…
Towards Continuous Sign Language Conversation from Isolated Signs
Youngmin Kim, Kyobin Choo, Jiwoo Park +4
Sign language is the primary language for many Deaf and Hard-of-Hearing (DHH) signers, yet most conversational AI systems still mediate interaction through spoken or written langua…
Rethinking Graph Convolution for 2D-to-3D Hand Pose Lifting
Chanyoung Kim, Donghyun Kim, Dong-Hyun Sim +2
Graph convolutional networks (GCNs) are widely used for 3D hand pose estimation, where the hand skeleton is encoded as a fixed adjacency graph. We revisit whether this is the most…
Real-Time Visual Attribution Streaming in Thinking Model
Seil Kang, Woojung Han, Junhyeok Kim +3
We present an amortized framework for real-time visual attribution streaming in multimodal thinking models. When these models generate code from a screenshot or solve math problems…
ViKey: Enhancing Temporal Understanding in Videos via Visual Prompting
Yeonkyung Lee, Dayun Ju, Youngmin Kim +2
Recent advancements in Video Large Language Models (VideoLLMs) have enabled strong performance across diverse multimodal video tasks. To reduce the high computational cost of proce…
Anchoring and Rescaling Attention for Semantically Coherent Inbetweening
Tae Eun Choi, Sumin Shim, Junhyeok Kim +1
Generative inbetweening (GI) seeks to synthesize realistic intermediate frames between the first and last keyframes beyond mere interpolation. As sequences become sparser and motio…
Interpretable Motion-Attentive Maps: Spatio-Temporally Localizing Concepts in Video Diffusion Transformers
Youngjun Jun, Seil Kang, Woojung Han +1
Video Diffusion Transformers (DiTs) have been synthesizing high-quality video with high fidelity from given text descriptions involving motion. However, understanding how Video DiT…
Rare Text Semantics Were Always There in Your Diffusion Transformer
Seil Kang, Woojung Han, Dayun Ju +1
Starting from flow- and diffusion-based transformers, Multi-modal Diffusion Transformers (MM-DiTs) have reshaped text-to-vision generation, gaining acclaim for exceptional visual f…
Interpreting Attention Heads for Image-to-Text Information Flow in Large Vision-Language Models
Jinyeong Kim, Seil Kang, Jiwoo Park +2
Large Vision-Language Models (LVLMs) answer visual questions by transferring information from images to text through a series of attention heads. While this image-to-text informati…
Interpreting vision transformers via residual replacement model
Jinyeong Kim, Junhyeok Kim, Yumin Shim +3
How do vision transformers (ViTs) represent and process the world? This paper addresses this long-standing question through the first systematic analysis of 6.6K features across al…
Mono-Modalizing Extremely Heterogeneous Multi-Modal Medical Image Registration
Kyobin Choo, Hyunkyung Han, Jinyeong Kim +2
In clinical practice, imaging modalities with functional characteristics, such as positron emission tomography (PET) and fractional anisotropy (FA), are often aligned with a struct…
Backbone Augmented Training for Adaptations
Jae Wan Park, Junhyeok Kim, Youngjun Jun +2
Adaptations facilitate efficient training of large backbone models, including diffusion models for image generation and transformer-based language models. While various adaptation…
WAVE: Warp-Based View Guidance for Consistent Novel View Synthesis Using a Single Image
Jiwoo Park, Tae Eun Choi, Youngjun Jun +1
Generating high-quality novel views of a scene from a single image requires maintaining structural coherence across different views, referred to as view consistency. While diffusio…
PRETI: Patient-Aware Retinal Foundation Model via Metadata-Guided Representation Learning
Yeonkyung Lee, Woojung Han, Youngjun Jun +3
Retinal foundation models have significantly advanced retinal image analysis by leveraging self-supervised learning to reduce dependence on labeled data while achieving strong gene…
Spatial Transport Optimization by Repositioning Attention Map for Training-Free Text-to-Image Synthesis
Woojung Han, Yeonkyung Lee, Chanyoung Kim +2
Diffusion-based text-to-image (T2I) models have recently excelled in high-quality image generation, particularly in a training-free manner, enabling cost-effective adaptability and…
Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding
Seil Kang, Jinyeong Kim, Junhyeok Kim +1
Visual grounding seeks to localize the image region corresponding to a free-form text description. Recently, the strong multimodal capabilities of Large Vision-Language Models (LVL…
See What You Are Told: Visual Attention Sink in Large Multimodal Models
Seil Kang, Jinyeong Kim, Junhyeok Kim +1
Large multimodal models (LMMs) "see" images by leveraging the attention mechanism between text and visual tokens in the transformer decoder. Ideally, these models should focus on k…
Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes
Donghyun Kim, Chanyoung Kim, Hyunah Ko +1
While 3D point clouds are widely used in vision applications, their irregular and sparse nature make them challenging to handle. In response, numerous encoding approaches have been…
Pathology-Aware Adaptive Watermarking for Text-Driven Medical Image Synthesis
Chanyoung Kim, Dayun Ju, Jinyeong Kim +3
As recent text-conditioned diffusion models have enabled the generation of high-quality images, concerns over their potential misuse have also grown. This issue is critical in the…