Tracking-based social behavior analysis of lab animals, especially mice, is crucial for research in biology, medicine, and psychology. However, existing visual tracking systems struggle to maintain long-term, accurate tracking due to frequent identity association errors, which lead to extensive manual correction and limit research scalability. This paper introduces mTrack, an RFID–vision hybrid mouse tracking system that leverages the precise identification capability of UHF RFID to assist the visual tracker, enabling long-term, self-correcting, and high-accuracy mouse tracking. Our experiments demonstrate that mTrack can track up to ten mice simultaneously with over 99.23% identification accuracy and a 0.4 cm 99th-percentile localization error, reducing error rate by more than 40x compared with visual tracking systems. Our field studies indicate that mTrack can sustain this performance for over two hours and be seamlessly integrated into existing animal behavior research workflows. The code and dataset are open-sourced at https://github.com/SOAR-PKU/mTrack.

We introduce ErLang Sight, a novel multimodal system designed for liquid identification, integrating vision-based object detection, millimeter-wave (mmWave) Synthetic Aperture Radar (SAR) imaging, and large language model (LLM)-based contextual reasoning. Initially, the system leverages a visual detection pipeline to identify potential liquid containers within the environment, subsequently directing a mmWave sensor to perform targeted SAR imaging of these identified regions, and the permittivity values of the liquids are estimated using reflection coefficient analysis techniques. These physical measurements, combined with visual context and environmental indicators (such as whether the scenario is a kitchen, laboratory, or bar), are then input into a pretrained LLM. The LLM employs advanced semantic and situational reasoning to accurately determine the most likely type of liquid by integrating physics-based data with contextual knowledge. Experimental evaluations demonstrate that ErLang Sight significantly enhances the accuracy of distinguishing visually ambiguous liquids and exhibits robust generalization to previously unseen environments.

This paper studies the problem of graph-level clustering, which is a novel yet challenging task. This problem is critical in a variety of real-world applications such as protein clustering and genome analysis in bioinformatics. Recent years have witnessed the success of deep clustering coupled with graph neural networks (GNNs). However, existing methods focus on clustering among nodes given a single graph, while exploring clustering on multiple graphs is still under-explored. In this paper, we propose a general graph-level clustering framework named Graph-Level Contrastive Clustering (GLCC) given multiple graphs. Specifically, GLCC first constructs an adaptive affinity graph to explore instance- and cluster-level contrastive learning (CL). Instance-level CL leverages graph Laplacian based contrastive loss to learn clustering-friendly representations while cluster-level CL captures discriminative cluster representations incorporating neighbor information of each sample. Moreover, we utilize neighbor-aware pseudo-labels to reward the optimization of representation learning. The two steps can be alternatively trained to collaborate and benefit each other. Experiments on a range of well-known datasets demonstrate the superiority of our proposed GLCC over competitive baselines.

The burgeoning field of smart agriculture is increasingly leveraging unmanned aerial vehicles (UAVs) for data collection. However, inadequate visual features and plant occlusion can hamper visual-based simultaneous localization and mapping (SLAM) of UAVs. As a potential solution, RFID can work as an efficient reference infrastructure, enabling a connection between aerial imagery and real-world contexts. Despite this promise, hurdles remain in attaining high-accuracy, high-throughput, and long-range RFID localization, as well as practical deployment of RFID tags and reader implementation on UAVs. Overcoming these challenges holds significant potential, particularly considering their impact on numerous applications, such as large-scale agricultural management and plant stand reduction detection.