6 accepted papers
The accelerating realism of AI-generated content has amplified the spread of deceptive information and eroded public trust. Prior works typically split the problem into two tracks, media authenticity, which concerns whether content is real or AI-generated, and content veracity, which concerns semant…
In an era dominated by multi-sensor fusion, this paper explores the operational limits of LiDAR-only odometry. We introduce Onion-LO++, which is designed to overcome two practical limitations of Onion-LO: poor performance in geometrically degenerate environments and instability under high-motion con…
LiDAR odometry is a fundamental technology for autonomous navigation. However, existing LiDAR-based odometry methods typically demand extensive manual parameter tuning and remain prone to instability when deployed across varying LiDAR types and environments. This letter focuses on the essence of poi…
Achieving fine-grained spatio-temporal understanding in videos remains a major challenge for current Video Large Multimodal Models (Video LMMs). Addressing this challenge requires mastering two core capabilities: video referring understanding, which captures the semantics of video regions, and video…
Soft robots, formulated by soft and compliant materials, have grown significantly in recent years toward safe and adaptable operations and interactions with dynamic environments. Modeling the complex, nonlinear behaviors and controlling the deformable structures of soft robots present challenges. Th
Image segmentation is a crucial vision task that groups pixels within an image into semantically meaningful segments, which is pivotal in obtaining a fine-grained understanding of real-world scenes. However, an increasing privacy concern exists regarding training large-scale image segmentation model…