12 accepted papers
Existing Multi-Camera Multi-Target (MCMT) tracking models typically adopt a two-stage framework, involving single-camera tracking followed by inter-camera tracking. However, in this paradigm, the use of multiple views is confined to recovering missed matches in the first stage, providing a limited c
Existing surrounding-view 3D object detectors initialize high-confidence queries using current 2D information, while leveraging historical 3D features as priors. However, such heavy reliance on 2D cues introduces spatio-temporal inconsistencies between 2D and 3D representations. Specifically, 2D cue
We present Autoregressive Representation Alignment (ARRA), a new training framework that unlocks global-coherent text-to-image generation in autoregressive LLMs without architectural modifications. Different from prior works that require complex architectural redesigns, ARRA aligns LLM
The capability of tracking objects in low-light environments like nighttime is crucial for numerous real-world applications such as crowd behavior analysis and traffic scene understanding. However, previous Multi-Camera Multi-Target(MCMT) tracking methods are primarily focused on tracking during day…
Learning robust and generalizable manipulation skills from few demonstrations remains a key challenge in robotics, with broad applications in industrial automation and service robotics. Although recent imitation learning methods have achieved impressive results, they often require a large amount of
The multi-modal 3D semantic occupancy task provides a comprehensive understanding of the scene and has received considerable attention in the field of autonomous driving. However, existing methods mainly focus on processing large-scale voxels, which bring high computational costs and degrade details…
We investigate LoRA in federated learning through the lens of the asymmetry analysis of the learned $A$ and $B$ matrices. In doing so, we uncover that $A$ matrices are responsible for learning general knowledge, while $B$ matrices focus on capturing client-specific knowledge. Based on this finding,…
3D Multi-Object Tracking (MOT) aims to establish and maintain consistent object trajectories in continuously dynamic environments. At present, the tracking-by-detection has emerged as a dominant paradigm for 3D MOT, due to its simplicity and efficiency. However, this paradigm depends heavily on the
Recent years have witnessed the remarkable progress of 3D multi-modality object detection methods based on the Bird's-Eye-View (BEV) perspective. However most of them overlook the complementary interaction and guidance between LiDAR and camera. In this work we propose a novel multi-modality 3D objec…
We propose residual denoising diffusion models (RDDM) a novel dual diffusion process that decouples the traditional single denoising diffusion process into residual diffusion and noise diffusion. This dual diffusion framework expands the denoising-based diffusion models initially uninterpretable for…
Underwater images suffer from color casts and low illumination due to the scattering and absorption of light as it propagates in water. These problems can interfere with underwater vision tasks, such as recognition and detection. We propose an adaptive learning attention network for underwater image
In this paper, we develop a novel 3D object recognition algorithm to perform detection and pose estimation jointly. We focus on analyzing the advantages of the 3D point cloud relative to the RGB-D image and try to eliminate the unpredictability of output values that inevitably occurs in regression t…