31 accepted papers
To enhance the precision of cancer prognosis, recent research has increasingly focused on multimodal survival methods by integrating genomic data and histology images. However, current approaches overlook the fact that the proteome serves as an intermediate layer bridging genomic alterations and his
Resting-state functional MRI (rs-fMRI) provides rich information for modeling brain connectivity in disease diagnosis. However, most existing brain graph learning methods rely solely on imaging data, leading to limited biological interpretability and poor integration of external medical knowledge. T
Video data provides a rich source beyond expensive action-labeled data for advancing robot learning. Recent approaches have demonstrated promising potential in leveraging video data by learning latent actions for policy training. The latent action tokenizer encodes latent actions between successive
In daily domestic settings, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this cha…
Outdoor loop closure detection is essential for mitigating accumulated drift in SLAM and generating a global consistent map. Semantic graph matching methods utilize object-level topology for distinctive scene representation but rely on environments with rich and distinguishable objects. Moreover, ac…
Learning from demonstration is a powerful method for robotic skill acquisition. However, the significant expense of collecting such action-labeled robot data presents a major bottleneck. Video data, a rich data source encompassing diverse behavioral and physical knowledge, emerges as a promising alt…
While autoregressive models demonstrate remarkable success in text and image generation, their application to robot policies suffers from weak holistic comprehension, cumulative errors, and limited multi-modal modeling capabilities, particularly in long-horizon tasks or multi-modal scenarios. This p
Learning from human demonstrations is an emerging trend for designing intelligent robotic systems. However, previous methods typically regard videos as instructions, simply dividing videos into action sequences for robotic repetition, which pose obstacles to generalization to diverse tasks or object
Image feature matching is a fundamental task in computer vision. Existing local feature matching methods can establish robust correspondences between image pairs. However, these methods heavily rely on dense local image features, making them susceptible to significant perspective differences, charac
In daily domestic settings, frequently used objects like <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cups</i> often have unfixed positions and multiple instances within the same category, and their carriers also frequently change. As a result, it
In everyday life, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this challenge, th
Loop closure detection and pose estimation play a significant role in correcting odometry trajectories and generating globally consistent point cloud maps. Geometric feature descriptor methods neglect object-level spatial topology features, resulting in inadequate performance in loop closure detecti
Point cloud registration is essential in computer vision and robotics. Recently, transformer-based methods have achieved advanced point cloud registration performance. However, the standard attention mechanism utilized in these methods considers many low-relevance points, and it has difficulty focus…
Collaborative perception facilitates a more comprehensive representation of the environment by leveraging complementary information shared among various agents and sensors. However, practical applications often encounter information disturbance which includes perception packet loss and time delays,…
Self-supervised depth estimation has attracted much attention due to its ability to improve the 3D perception capabilities of unmanned systems. However, existing unsupervised frameworks rely on the assumption of photometric consistency, which may not hold in challenging environments such as night-ti…
Visual imitation learning (VIL) provides an efficient and intuitive strategy for robotic systems to acquire novel skills. Recent advancements in Vision Language Models (VLMs) have demonstrated remarkable performance in vision and language reasoning capabilities for VIL tasks. Despite the progress, c…
Photo-realistic style transfer aims at migrating the artistic style from an exemplar style image to a content image, producing a result image without spatial distortions or unrealistic artifacts. Impressive results have been achieved by recent deep models. However, deep neural network based methods…
Automated Valet Parking (AVP) has been exten-sively researched as an important application of autonomous driving. Considering the high dynamics and density of real parking lots, a system that considers multiple vehicles simultaneously is more robust and efficient than a single vehicle setting as in…
Point cloud registration is a crucial technology in the fields of robotics and computer vision. Despite the significant advances in point cloud registration enabled by Transformer-based methods, limitations persist due to indistinct feature extraction, noise sensitivity, and outlier handling. These…
Filtering out outlier data associations between local maps can improve the robustness and accuracy of multi-robot localization. When the overlap is low and the field of view difference is large, it is likely to produce outlier data associations between local maps, which will reduce the matching accu…
Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks. Inspired by the advancements of the GPT, we present PointGPT, a novel approach that extends the concept of GPT to point clouds, a…
Point cloud registration is essential in computer vision and robotics. In this paper, a critical observation is made that the invisible parts of each point cloud can be directly utilized as inherent masks, and the aligned point cloud pair can be regarded as the reconstruction target. Motivated by th…
Capturing the semantics of objects and the topological relationship allows the robot to describe the scene more intelligently like a human and measure the similarity between scenes (loop closure detection) more accurately. However, many current semantic graph matching methods are based on walk descr…
The collaborative operation of multiple robots can make up for the shortcomings of a single robot, such as limited field of perception or sensor failure. multirobots collaborative semantic mapping can enhance their comprehensive contextual understanding of the environment. However, existing multirob…
Autonomous robots are often required to acquire high-level prior knowledge by continuously reconstructing the semantics and geometry of the surrounding scene, which is the basis of exploration and planning. Most existing continuous semantic mapping algorithms cannot distinguish potential differences…
Camera-LiDAR fusion provides precise distance measurements and fine-grained textures, making it a promising option for 3D vehicle detection in autonomous driving scenarios. Previous camera-LiDAR based 3D vehicle detection approaches mainly focused on employing image-based pre-trained models to fetch
Fast arbitrary neural style transfer has attracted widespread attention from academic, industrial and art communities due to its flexibility in enabling various applications. Existing solutions either attentively fuse deep style feature into deep content feature without considering feature distribut…
The matching and fusing of local maps generated by multiple robots can greatly enhance the performance of relative localization and collaborative mapping. Currently, existing semantic matching methods are partly based on classical iterative closet point (ICP), which typically fail in cases with larg…
In unstructured and unknown environments, heterogeneous robots must be able to perceive the environment, coordinate with each other and complete tasks collaboratively with onboard sensors. In this paper, a tightly-coupled perception and navigation framework is proposed for heterogeneous land-air rob…
Existing autonomous parking solutions usually require special signs, pre-built maps or accurate ranging sensors to achieve reliable perception of the parking environment, but these methods are difficult to popularize because they either require preconditions or are expensive for production cars. In…
The detection and tracking of dynamic traffic participants (e.g., pedestrians, cars, and bicyclists) plays an important role in reliable decision-making and intelligent navigation for autonomous vehicles. However, due to the rapid movement of the target, most current vision-based tracking methods, w…