27 accepted papers
Spatial transcriptomics (ST) bridges gene expression and tissue morphology but faces clinical adoption barriers due to technical complexity and prohibitive costs. While computational methods predict gene expression from H&E-stained whole-slide images (WSIs), existing approaches often fail to capture
Robust 3D representation learning forms the perceptual foundation of spatial intelligence, enabling downstream tasks in scene understanding and embodied AI. However, learning such representations directly from unposed multi-view images remains challenging. Recent self-supervised methods attempt to u
Evaluation benchmarks play a central role in assessing vision–language models (VLMs). However, most existing multimodal benchmarks are static, making them increasingly vulnerable to data contamination, temporal staleness, and high construction costs. In this work, we introduce MMBench-Live, a multi-…
The success of loop closure and global map consistency largely relies on robust place recognition, making it a safety-critical capability for mobile robots in autonomous navigation. However, most existing approaches prioritize recognition accuracy while overlooking uncertainty estimation, which may
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
Knowledge distillation (KD) aims to improve the performance of lightweight student networks under the guidance of pre-trained teachers. However, the large capacity gap between teachers and students limits the distillation gains. Previous methods addressing this problem have two weaknesses. First, mo…
Understanding human-to-human interactions, especially in contexts like public security surveillance, is critical for monitoring and maintaining safety. Traditional activity recognition systems are limited by fixed vocabularies, predefined labels, and rigid interaction categories that often rely on c…
Strategic mining attacks, such as selfish mining, exploit blockchain consensus protocols by deviating from honest behavior to maximize rewards. Markov Decision Process (MDP) analysis faces scalability challenges in modern digital economics, including blockchain. To address these limitations, reinfor
We present UNIALIGN, a unified model to align an arbitrary number of modalities (\text e.g. , image, text, audio, 3D point cloud, etc.) through one encoder and a single training phase. Existing solutions typically employ distinct encoders for each modality, resulting in increased parameters as the…
Bipedal robots, due to their anthropomorphic design, offer substantial potential across various applications, yet their control is hindered by the complexity of their structure. Currently, most research focuses on proprioception-based methods, which lack the capability to overcome complex terrain. W
We present a novel local-global feature fusion framework for body-weight exercise recognition with floor-based dynamic pressure maps. One step further from the existing studies using deep neural networks mainly focusing on global feature extraction, the proposed framework aims to combine local and g…
The motion distortion in LiDAR scans caused by the robot's aggressive motion and environmental terrain features significantly impacts the positioning and mapping performance of 3D LiDAR odometry. Existing distortion correction solutions struggle to balance computational complexity and accuracy. In t
Bilevel programs (BPs) find a wide range of applications in fields such as energy, transportation, and machine learning. As compared to BPs with continuous (linear/convex) optimization problems in both levels, the BPs with discrete decision variables have received much less attention, largely due to…
The radio map, which describes spatial signal strength and network coverage information, is crucial in modern wireless systems for network planning and resource management. Fine-grained radio maps rely on measurements collected by sparsely deployed spectrum sensors in the area of interest. However,…
Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI's clinical adoption. A certain number…
Understanding intention behind event processes in texts is important to many applications. One challenging task in this line is event process typing, which aims to tag the process with one action label and one object label describing the overall action of the process and object the process likely af…
This work presents an active software instrument allowing deep learning architects to interactively inspect neural network models' output behavior from user-manipulated values in any latent layer. Latent Inspector offers multiple dimension reduction techniques to visualize the model's high dimension…
Identifying moving objects is an essential capability for autonomous systems, as it provides critical information for pose estimation, navigation, collision avoidance, and static map construction. In this letter, we present MotionBEV, a fast and accurate framework for LiDAR moving object segmentatio
*In-context learning* is the paradigm that adapts large language models to downstream tasks by providing a few examples. *Few-shot selection*---selecting appropriate examples for each test instance separately---is important for in-context learning. In this paper, we propose **Skill-KNN**, a skill-ba…
To fully evaluate the overall performance of different NLP models in a given domain, many evaluation benchmarks are proposed, such as GLUE, SuperGLUE and CLUE. The field of natural language understanding has traditionally focused on benchmarks for various tasks in languages such as Chinese, English,…
Being able to infer possible events related to a specific target is critical to natural language processing. One challenging task in this line is event sequence prediction, which aims at predicting a sequence of events given a goal. Currently existing approach models this task as a statistical induc…
Generating temporally-ordered event sequences in texts is important to natural language processing. Two emerging tasks in this direction are temporal event ordering (rearranging the set of events to correct order) and event infilling (generating an event at a specified position). To tackle the two r…
Although the navigation of robots in urban environments has achieved great performance, there is still a problem of insufficient robustness in cross-scene (ground, water surface) navigation applications. An intuitive idea is to introduce multi-modal complementary data to improve the robustness of th
Recently reinforcement learning (RL) has emerged as a promising approach for quadrupedal locomotion, which can save the manual effort in conventional approaches such as designing skill-specific controllers. However, due to the complex nonlinear dynamics in quadrupedal robots and reward sparsity, it
Learning manipulation skills from observing human demonstration videos is a promising aspect for intelligent robotic systems. Recent advances in video to command provide an end-to-end approach to translate a video into robot plans. However, the general video captioning methods focus more on the unde…
MRI with multiple protocols is commonly used for diagnosis, but it suffers from a long acquisition time, which yields the image quality vulnerable to say motion artifacts. To accelerate, various methods have been proposed to reconstruct full images from under-sampled k-space data. However, these alg…
Combining deep neural networks with reinforcement learning has shown great potential in the next-generation intelligent control. However, there are challenges in terms of safety and cost in practical applications. In this pa- per, we propose the Intervention Aided Reinforcement Learning (IARL) frame