Multi-view Graph Condensation via Tensor Decomposition

Graph Neural Networks (GNNs) have demonstrated remarkable results in various real-world applications, including drug discovery, object detection, social media analysis, recommender systems, and text classification. In contrast to their vast potential, training them on large-scale graphs presents significant computational challenges due to the resources required for their storage and processing. Graph Condensation has emerged as a promising solution to reduce these demands by learning a synthetic compact graph that preserves the essential information of the original one while maintaining the GNN's predictive performance. Despite their efficacy, current graph condensation approaches frequently rely on a computationally intensive bi-level optimization. Moreover, they fail to maintain a mapping between synthetic and original nodes, limiting the interpretability of the model's decisions. In this sense, a wide range of decomposition techniques have been applied to learn linear or multi-linear functions from graph data, offering a more transparent and less resource-intensive alternative. However, their applicability to graph condensation remains unexplored. This paper addresses this gap and proposes a novel method called Multi-view Graph Condensation via Tensor Decomposition (GCTD) to investigate to what extent such techniques can synthesize an informative smaller graph and achieve comparable downstream task performance. Extensive experiments on six real-world datasets demonstrate that GCTD effectively reduces graph size while preserving GNN performance, achieving up to a 4.0\ improvement in accuracy on three out of six datasets and competitive performance on large graphs compared to existing approaches. Our code is available at https://anonymous.4open.science/r/gctd-345A.

FakeParts: a New Family of AI-Generated DeepFakes

We introduce FakeParts, a new class of deepfakes characterized by subtle, localized manipulations to specific spatial regions or temporal segments of otherwise authentic videos. Unlike fully synthetic content, these partial manipulations, ranging from altered facial expressions to object substitutions and background modifications, blend seamlessly with real elements, making them particularly deceptive and difficult to detect. To address the critical gap in detection capabilities, we present FakePartsBench, the first large-scale benchmark dataset specifically designed to capture the full spectrum of partial deepfakes. Comprising over 25K videos with pixel-level and frame-level manipulation annotations, our dataset enables comprehensive evaluation of detection methods. Our user studies demonstrate that FakeParts reduces human detection accuracy by over 30% compared to traditional deepfakes, with similar performance degradation observed in state-of-the-art detection models. This work identifies an urgent vulnerability in current deepfake detection approaches and provides the necessary resources to develop more robust methods for partial video manipulations.

UltraTac: Integrated Ultrasound-Augmented Visuotactile Sensor for Enhanced Robotic Perception

Visuotactile sensors provide high-resolution tactile information but are incapable of perceiving the material features of objects. We present UltraTac, an integrated sensor that combines visuotactile imaging with ultrasound sensing through a coaxial optoacoustic architecture. The design shares structural components and achieves consistent sensing regions for both modalities. Additionally, we incorporate acoustic matching into the traditional visuotactile sensor structure, enabling integration of the ultrasound sensing modality without compromising visuotactile performance. Through tactile feedback, we dynamically adjust the operating state of the ultrasound module to achieve flexible functional coordination. Systematic experiments demonstrate three key capabilities: proximity sensing in the 3-8 cm range ($R^2=0.90$), material classification (average accuracy: 99.20%), and texture-material dual-mode object recognition achieving 92.11% accuracy on a 15-class task. Finally, we integrate the sensor into a robotic manipulation system to concurrently detect container surface patterns and internal content, which verifies its potential for advanced human-machine interaction and precise robotic manipulation.

A bag of tricks for real-time Mitotic Figure detection

Mitotic figure (MF) detection in histopathology images is challenging due to large variations in slide scanners, staining protocols, tissue types, and the presence of artifacts. This paper presents a collection of training techniques - a bag of tricks - that enable robust, real-time MF detection across diverse domains. We build on the efficient RTMDet single stage object detector to achieve high inference speed suitable for clinical deployment. Our method addresses scanner variability and tumor heterogeneity via extensive multi-domain training data, balanced sampling, and careful augmentation. Additionally, we employ targeted, hard negative mining on necrotic and debris tissue to reduce false positives. In a grouped 5-fold cross-validation across multiple MF datasets, our model achieves an F1 score between 0.78 and 0.84. On the preliminary test set of the MItosis DOmain Generalization (MIDOG) 2025 challenge, our single-stage RTMDet-S based approach reaches an F1 of 0.81, outperforming larger models and demonstrating adaptability to new, unfamiliar domains. The proposed solution offers a practical trade-off between accuracy and speed, making it attractive for real-world clinical adoption.

Advancing Weakly-Supervised Change Detection in Satellite Images via Adversarial Class Prompting

Weakly-Supervised Change Detection (WSCD) aims to distinguish specific object changes (e.g., objects appearing or disappearing) from background variations (e.g., environmental changes due to light, weather, or seasonal shifts) in paired satellite images, relying only on paired image (i.e., image-level) classification labels. This technique significantly reduces the need for dense annotations required in fully-supervised change detection. However, as image-level supervision only indicates whether objects have changed in a scene, WSCD methods often misclassify background variations as object changes, especially in complex remote-sensing scenarios. In this work, we propose an Adversarial Class Prompting (AdvCP) method to address this co-occurring noise problem, including two phases: a) Adversarial Prompt Mining: After each training iteration, we introduce adversarial prompting perturbations, using incorrect one-hot image-level labels to activate erroneous feature mappings. This process reveals co-occurring adversarial samples under weak supervision, namely background variation features that are likely to be misclassified as object changes. b) Adversarial Sample Rectification: We integrate these adversarially prompt-activated pixel samples into training by constructing an online global prototype. This prototype is built from an exponentially weighted moving average of the current batch and all historical training data. Our AdvCP can be seamlessly integrated into current WSCD methods without adding additional inference cost. Experiments on ConvNet, Transformer, and Segment Anything Model (SAM)-based baselines demonstrate significant performance enhancements. Furthermore, we demonstrate the generalizability of AdvCP to other multi-class weakly-supervised dense prediction scenarios. Code is available at https://github.com/zhenghuizhao/AdvCP

Multiscale Video Transformers for Class Agnostic Segmentation in Autonomous Driving

Ensuring safety in autonomous driving is a complex challenge requiring handling unknown objects and unforeseen driving scenarios. We develop multiscale video transformers capable of detecting unknown objects using only motion cues. Video semantic and panoptic segmentation often relies on known classes seen during training, overlooking novel categories. Recent visual grounding with large language models is computationally expensive, especially for pixel-level output. We propose an efficient video transformer trained end-to-end for class-agnostic segmentation without optical flow. Our method uses multi-stage multiscale query-memory decoding and a scale-specific random drop-token to ensure efficiency and accuracy, maintaining detailed spatiotemporal features with a shared, learnable memory module. Unlike conventional decoders that compress features, our memory-centric design preserves high-resolution information at multiple scales. We evaluate on DAVIS'16, KITTI, and Cityscapes. Our method consistently outperforms multiscale baselines while being efficient in GPU memory and run-time, demonstrating a promising direction for real-time, robust dense prediction in safety-critical robotics.

Unobtrusive In-Situ Measurement of Behavior Change by Deep Metric Similarity Learning of Motion Patterns

This paper introduces an unobtrusive in-situ measurement method to detect user behavior changes during arbitrary exposures in XR systems. Here, such behavior changes are typically associated with the Proteus effect or bodily affordances elicited by different avatars that the users embody in XR. We present a biometric user model based on deep metric similarity learning, which uses high-dimensional embeddings as reference vectors to identify behavior changes of individual users. We evaluate our model against two alternative approaches: a (non-learned) motion analysis based on central tendencies of movement patterns and subjective post-exposure embodiment questionnaires frequently used in various XR exposures. In a within-subject study, participants performed a fruit collection task while embodying avatars of different body heights (short, actual-height, and tall). Subjective assessments confirmed the effective manipulation of perceived body schema, while the (non-learned) objective analyses of head and hand movements revealed significant differences across conditions. Our similarity learning model trained on the motion data successfully identified the elicited behavior change for various query and reference data pairings of the avatar conditions. The approach has several advantages in comparison to existing methods: 1) In-situ measurement without additional user input, 2) generalizable and scalable motion analysis for various use cases, 3) user-specific analysis on the individual level, and 4) with a trained model, users can be added and evaluated in real time to study how avatar changes affect behavior.

Quantitative Outcome-Oriented Assessment of Microsurgical Anastomosis

Microsurgical anastomosis demands exceptional dexterity and visuospatial skills, underscoring the importance of comprehensive training and precise outcome assessment. Currently, methods such as the outcome-oriented anastomosis lapse index are used to evaluate this procedure. However, they often rely on subjective judgment, which can introduce biases that affect the reliability and efficiency of the assessment of competence. Leveraging three datasets from hospitals with participants at various levels, we introduce a quantitative framework that uses image-processing techniques for objective assessment of microsurgical anastomoses. The approach uses geometric modeling of errors along with a detection and scoring mechanism, enhancing the efficiency and reliability of microsurgical proficiency assessment and advancing training protocols. The results show that the geometric metrics effectively replicate expert raters' scoring for the errors considered in this work.

Decomposing Behavioral Phase Transitions in LLMs: Order Parameters for Emergent Misalignment

Fine-tuning LLMs on narrowly harmful datasets can lead to behavior that is broadly misaligned with respect to human values. To understand when and how this emergent misalignment occurs, we develop a comprehensive framework for detecting and characterizing rapid transitions during fine-tuning using both distributional change detection methods as well as order parameters that are formulated in plain English and evaluated by an LLM judge. Using an objective statistical dissimilarity measure, we quantify how the phase transition that occurs during fine-tuning affects multiple aspects of the model. In particular, we assess what percentage of the total distributional change in model outputs is captured by different aspects, such as alignment or verbosity, providing a decomposition of the overall transition. We also find that the actual behavioral transition occurs later in training than indicated by the peak in the gradient norm alone. Our framework enables the automated discovery and quantification of language-based order parameters, which we demonstrate on examples ranging from knowledge questions to politics and ethics.

Context-Aware Risk Estimation in Home Environments: A Probabilistic Framework for Service Robots

We present a novel framework for estimating accident-prone regions in everyday indoor scenes, aimed at improving real-time risk awareness in service robots operating in human-centric environments. As robots become integrated into daily life, particularly in homes, the ability to anticipate and respond to environmental hazards is crucial for ensuring user safety, trust, and effective human-robot interaction. Our approach models object-level risk and context through a semantic graph-based propagation algorithm. Each object is represented as a node with an associated risk score, and risk propagates asymmetrically from high-risk to low-risk objects based on spatial proximity and accident relationship. This enables the robot to infer potential hazards even when they are not explicitly visible or labeled. Designed for interpretability and lightweight onboard deployment, our method is validated on a dataset with human-annotated risk regions, achieving a binary risk detection accuracy of 75%. The system demonstrates strong alignment with human perception, particularly in scenes involving sharp or unstable objects. These results underline the potential of context-aware risk reasoning to enhance robotic scene understanding and proactive safety behaviors in shared human-robot spaces. This framework could serve as a foundation for future systems that make context-driven safety decisions, provide real-time alerts, or autonomously assist users in avoiding or mitigating hazards within home environments.