179 citations · 382 across the 62 of their papers we have counts for
35 papers · 1 filter
Not Every Correction Helps: Gain-Guided Continual Test-Time Adaptation
Youjia Zhang, Huiling Liu, Soyun Choi +2
Continual test-time adaptation (CTTA) adapts a source model to an unlabeled test stream whose distribution may change over time. Existing TTA methods often assess prediction reliab…
MuseBench: Benchmarking Intent-Level Audiovisual Arts Understanding in MLLMs
Yuxuan Fan, Gyusik Seo, Jing Hao +3
Audiovisual arts encompass diverse creative disciplines, including cinema, visual arts, stage performance, and game design, where artistic meaning arises from deliberate combinatio…
Confidence-Aware Tool Orchestration for Robust Video Understanding
Yangfan He, Yujin Choi, Jaehong Yoon
Video reasoning language models implicitly assume that every input frame is equally reliable. This leads to what we term the Blind Trust Problem: under realistic perturbations such…
Safe Few-Step Generation via Velocity Editing
Yujin Choi, Jaehong Yoon
Flow matching has recently emerged as a strong paradigm for state-of-the-art text-to-image (T2I) generation, enabling high-quality generation with a small number of sampling steps.…
VGGT-Edit: Feed-forward Native 3D Scene Editing with Residual Field Prediction
Kaixin Zhu, Yiwen Tang, Yifan Yang +9
High-quality 3D scene reconstruction has recently advanced toward generalizable feed-forward architectures, enabling the generation of complex environments in a single forward pass…
PhyMotion: Structured 3D Motion Reward for Physics-Grounded Human Video Generation
Yidong Huang, Zun Wang, Han Lin +6
Generating realistic human motion is a central yet unsolved challenge in video generation. While reinforcement learning (RL)-based post-training has driven recent gains in general…
EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding
Ziyang Wang, Yue Zhang, Shoubin Yu +6
Next-generation visual assistants, such as smart glasses, embodied agents, and always-on life-logging systems, must reason over an entire day or more of continuous visual experienc…
Are Video Reasoning Models Ready to Go Outside?
Yangfan He, Changgyu Boo, Jaehong Yoon
In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion. Under such conditions, their understanding and reasonin…
AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
Zun Wang, Han Lin, Jaehong Yoon +3
Maintaining spatial world consistency over long horizons remains a central challenge for camera-controllable video generation. Existing memory-based approaches often condition gene…
When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning
Shoubin Yu, Yue Zhang, Zun Wang +4
Despite rapid progress in MLLMs, visual spatial reasoning remains unreliable when correct answers depend on how a scene would appear under unseen or alternative viewpoints. Recent…
Hierarchy-Aware Multimodal Unlearning for Medical AI
Fengli Wu, Vaidehi Patil, Jaehong Yoon +2
Pretrained Multimodal Large Language Models (MLLMs) are increasingly used in sensitive domains such as medical AI, where privacy regulations like HIPAA and GDPR require specific re…
Self-Refining Video Sampling
Sangwon Jang, Taekyung Ki, Jaehyeong Jo +3
Modern video generators still struggle with complex physical dynamics, often falling short of physical realism. Existing approaches address this using external verifiers or additio…
WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning
Woongyeong Yeo, Kangsan Kim, Jaehong Yoon +1
Recent advances in video large language models have demonstrated strong capabilities in understanding short clips. However, scaling them to hours- or days-long videos remains highl…
Planning with Sketch-Guided Verification for Physics-Aware Video Generation
Yidong Huang, Zun Wang, Han Lin +5
Recent video generation approaches increasingly rely on planning intermediate control signals such as object trajectories to improve temporal coherence and motion fidelity. However…
Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning
Ziyang Wang, Jaehong Yoon, Shoubin Yu +3
Despite advances in reinforcement learning (RL)-based video reasoning with large language models (LLMs), data collection and fine-tuning remain significant challenges. These method…
How Well Do Vision-Language Models Understand Movies?
Emmanouil Zaranis, António Farinhas, Saul Santos +28
Vision-language models (VLMs) can increasingly handle extended contexts and answer questions about long videos--but can they understand the narrative structure of movies as humans…
MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation
Shoubin Yu, Yue Zhang, Ziyang Wang +2
Combining pre-trained expert models offers substantial potential for scalable multimodal reasoning, but building a unified framework remains challenging due to the increasing diver…
Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Models
Sangwon Jang, Taekyung Ki, Jaehyeong Jo +4
Advancements in diffusion models have significantly improved video quality, directing attention to fine-grained controllability. However, many existing methods depend on fine-tunin…
Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning
Daeun Lee, Jaehong Yoon, Jaemin Cho +1
Recent advances in Chain-of-Thought (CoT) reasoning have improved complex video understanding, but existing methods often struggle to adapt to domain-specific skills (e.g., event d…
EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance
Zun Wang, Jaemin Cho, Jialu Li +4
Recent approaches for video generation with camera control often create anchor videos (i.e., rendered videos that approximate desired camera motions) to guide diffusion models as a…
Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization
Jialu Li, Shoubin Yu, Han Lin +3
Recent advancements in text-to-video (T2V) diffusion models have significantly enhanced the visual quality of the generated videos. However, even recent T2V models find it challeng…
DreamRunner: Fine-Grained Compositional Story-to-Video Generation with Retrieval-Augmented Motion Adaptation
Zun Wang, Jialu Li, Han Lin +2
Storytelling video generation (SVG) aims to produce coherent and visually rich multi-scene videos that follow a structured narrative. Existing methods primarily employ LLM for high…
Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement
Daeun Lee, Jaehong Yoon, Jaemin Cho +1
Recent text-to-video (T2V) diffusion models have made remarkable progress in generating high-quality videos. However, they often struggle to align with complex text prompts, partic…
SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generation
Jaehong Yoon, Shoubin Yu, Vaidehi Patil +2
Recent advances in diffusion models have significantly enhanced their ability to generate high-quality images and videos, but they have also increased the risk of producing unsafe…