18 papers · 1 filter
Triggering Generalist Reasoning via Predictive Uncertainty for Dual-System VLA
Hyemin Yang, Wooseong Jeong, Giwon Lee +1
Dual-system Vision-Language-Action (VLA) models improve real-time robotic control by pairing a slow, reasoning-capable generalist with a fast specialist action expert. However, exi…
VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation
Hoonhee Cho, Jae-Young Kang, Giwon Lee +3
End-to-end autonomous driving in urban environments requires robust decision-making under partial observability and complex multi-agent interactions. Severe occlusions and dense tr…
DINOde: Continuous Vision-Text Alignment for Open-Vocabulary Semantic Segmentation
Sung-Hoon Yoon, Hoyong Kwon, Changgyoon Oh +1
Open-vocabulary semantic segmentation (OVSS) leverages textual semantics to segment objects beyond predefined categories. While the self-supervised model DINOv3 provides strong str…
Ego-Human Motion Prediction with 3D-Aware LLM
Yujin Bae, Jaewoo Jeong, Hyeonseong Kim +1
Anticipating human motion from an egocentric perspective is fundamental for proactive assistance in AR/VR, human-robot collaboration, and embodied AI. While recent works incorporat…
RAF: Reliability-Aware Fusion of Camera, LiDAR, and 4D RADAR for Robust 3D Object Detection in Adverse Weather
Heejun Park, Jaeseok Jeong, Kuk-Jin Yoon
Robust 3D object detection in adverse weather conditions is challenging due to sensor limitations. Although combining complementary modalities such as LiDAR and 4D RADAR has shown…
Rank-Aware Hyperbolic Alignment for Vision-Language Dataset Distillation
Jongoh Jeong, Sun-Kyung Lee, Kuk-Jin Yoon
Vision-language dataset distillation (VLDD) compresses a large image-text paired dataset into a small set of synthetic pairs that can efficiently train contrastive vision-language…
Multimodal Distribution Matching for Vision-Language Dataset Distillation
Jongoh Jeong, Hoyong Kwon, Minseok Kim +1
Dataset distillation compresses large training sets into compact synthetic datasets while preserving downstream performance. As modern systems increasingly operate on paired vision…
Task-oriented Learnable Diffusion Timesteps for Universal Few-shot Learning of Dense Tasks
Changgyoon Oh, Jongoh Jeong, Jegyeong Cho +1
Denoising diffusion probabilistic models have brought tremendous advances in generative tasks, achieving state-of-the-art performance thus far. Current diffusion model-based applic…
AVOID: The Adverse Visual Conditions Dataset with Obstacles for Driving Scene Understanding
Jongoh Jeong, Taek-Jin Song, Jong-Hwan Kim +1
Understanding road scenes for visual perception remains crucial for intelligent self-driving cars. In particular, it is desirable to detect unexpected small road hazards reliably i…
Exploring Syn-to-Real Domain Adaptation for Military Target Detection
Jongoh Jeong, Youngjin Oh, Gyeongrae Nam +2
Object detection is one of the key target tasks of interest in the context of civil and military applications. In particular, the real-world deployment of target detection methods…
Generative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model
Daehee Park, Monu Surana, Pranav Desai +3
While data-driven trajectory prediction has enhanced the reliability of autonomous driving systems, it still struggles with rarely observed long-tail scenarios. Prior works address…
Improving Black-Box Generative Attacks via Generator Semantic Consistency
Jongoh Jeong, Hunmin Yang, Jaeseok Jeong +1
Transfer attacks optimize on a surrogate and deploy to a black-box target. While iterative optimization attacks in this paradigm are limited by their per-input cost limits efficien…
Multi-modal Knowledge Distillation-based Human Trajectory Forecasting
Jaewoo Jeong, Seohee Lee, Daehee Park +2
Pedestrian trajectory forecasting is crucial in various applications such as autonomous driving and mobile robot navigation. In such applications, camera-based perception enables t…
FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks
Hunmin Yang, Jongoh Jeong, Kuk-Jin Yoon
Deep neural networks are known to be vulnerable to security risks due to the inherent transferable nature of adversarial examples. Despite the success of recent generative model-ba…
Prompt-Driven Contrastive Learning for Transferable Adversarial Attacks
Hunmin Yang, Jongoh Jeong, Kuk-Jin Yoon
Recent vision-language foundation models, such as CLIP, have demonstrated superior capabilities in learning representations that can be transferable across diverse range of downstr…
Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioning
Jaewoo Jeong, Daehee Park, Kuk-Jin Yoon
Human pose forecasting garners attention for its diverse applications. However, challenges in modeling the multi-modal nature of human motion and intricate interactions among agent…
T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory
Daehee Park, Jaeseok Jeong, Sung-Hoon Yoon +2
Trajectory prediction is a challenging problem that requires considering interactions among multiple actors and the surrounding environment. While data-driven approaches have been…
Improving Transferability for Cross-domain Trajectory Prediction via Neural Stochastic Differential Equation
Daehee Park, Jaewoo Jeong, Kuk-Jin Yoon
Multi-agent trajectory prediction is crucial for various practical applications, spurring the construction of many large-scale trajectory datasets, including vehicles and pedestria…