20 accepted papers
Cross-modal transfer methods have achieved significant progress in extending RGB-based foundation models to non-RGB modalities. However, existing transfer paradigms are primarily task-oriented, meaning that changing tasks requires re-training and re-storing, leading to substantial redundancy in data
Polarization information is valuable for many computer vision applications. However, in hazy environments, polarization information is severely attenuated due to the degradation of captured polarized images. Existing dehazing methods struggle to effectively restore polarization information, as singl…
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
Sparse point-based trackers struggle with textureless and incomplete point clouds. Conversely, dense voxel-based trackers have richer spatial and semantic information, but how to filter out interference from complex backgrounds remains a challenge. Besides, there is still a gap between point and vox
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
3D single object tracking based on point clouds is a key challenge in robotics and autonomous driving technology. Mainstream methods rely on point clouds for geometric matching or motion estimation between the target template and the search area. However, the lack of texture and the sparsity of inco
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…
Perception systems are crucial for the safe operation of autonomous vehicles, particularly for 3D object detection. While LiDAR-based methods are limited by adverse weather conditions, 4D radars offer promising all-weather capabilities. However, 4D radars introduce challenges such as extreme sparsit
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…
Sparse point-based trackers struggle with texture-less and incomplete point clouds. Conversely, dense voxel-based trackers have richer spatial and semantic information, but filtering out interference from complex backgrounds remains a challenge. Additionally, there is still a gap between point and v
3D single object tracking (SOT) based on point cloud has attracted much attention due to its important role in machine vision and autonomous driving. Recently, M2-Track proposes a two-stage tracking structure centered on motion, but they ignore the effect of segmentation errors in sparse point cloud…
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…
LiDAR-based 3D single object tracking has received remarkable attention due to its crucial role in robotics and autonomous driving. Most of them are based on hierarchical feature structures from PointNet++. However, existing based-stratified structure trackers ignore the fact that non-linearities in
Visual mask learning has received increasing attention in the field of visual object tracking. However, most existing studies merely utilize visual mask learning works as pre-training models without fully exploiting their potential for visual representation. In this paper, we present a novel approac…
Underwater object detection has attracted increasing interest for its wide application in various underwater tasks. However, due to underwater image quality degradation and the lack of large-scale underwater object datasets, many underwater detectors suffer from low detection performance. To address…
Recent models based on encoder-decoder architecture have shown excellent performance in visual object tracking. The encoder models the global spatiotemporal feature correlation between the template and the search regions, while the decoder learns query embeddings to predict the spatial location of t…
Single-source domain generalization (SDG) for object detection is a challenging yet essential task as the distribution bias of the unseen domain degrades the algorithm performance significantly. However existing methods attempt to extract domain-invariant features neglecting that the biased data lea…
3D single object tracking in point clouds is an essential task in robotics and autonomous driving. Many astonished trackers only adopt the voting-based region proposal network (RPN) to regress the object's location. However, they suffer from heavy outlier votes and ignore the global semantic feature
Some impressing multi-kernel or multi-task correlation filter trackers only focus on boosting the discrimination of multi-channel features, or exploiting the interdependence among different tasks. However, the cooperation and complementary of both technologies are missed, and the spatial structure a…
Due to the complex underwater environment, underwater imaging often encounters some problems such as blur, scale variation, color shift, and texture distortion. Generic detection algorithms can not work well when we use them directly in the underwater scene. To address these problems, we propose an…