Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while remaining computationally efficient. However, existing approaches generally struggle to reconcile fine-grained representation learning, especially under class imbalance and real-world constraints. In this paper, we present FreSH, a Frequency-Segmented Hierarchical Multi-Expert Framework designed to address these challenges. FreSH introduces a new perspective for MTSC by enabling adaptive, multi-scale analysis of temporal signals, allowing different aspects of the data to be modeled in a complementary and coordinated manner. By combining localized specialization with holistic context modeling, FreSH achieves strong representational capacity without incurring excessive computational overhead. An adaptive fusion strategy further enhances flexibility, enabling the model to dynamically emphasize the most informative components of the input. In addition, we incorporate a more robust optimization objective that improves learning stability across varying sample difficulties and class distributions. Extensive evaluations on 30 UEA benchmark datasets and real-world vibration data demonstrate that FreSH consistently outperforms state-of-the-art methods in classification accuracy, while substantially reducing model size and efficiency. The implementation code is publicly available at https://github.com/Wangmy2120/FreSH00.
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Image quality is critical for accurate medical diagnosis. However, MRI, CT, and ultrasound images are often of low resolution and quality due to cost constraints, complicating the visualization of key anatomical structures and lesions. While such limitations are common in practice, traditional methods treat image enhancement as a separate preprocessing step, failing to fully leverage its potential synergy with image segmentation. To address this, we propose DiSIINet (Diffusion-based Symbiotic Information Interaction Network), which is built on the principle that enhancement and segmentation should mutually reinforce each other in a unified model. Based on Denoising Diffusion Implicit Models (DDIM), DiSIINet integrates an enhancement branch and a segmentation branch. These branches interact through a novel Symbiotic Information Interaction (SII) module, which facilitates dynamic, feature-level information exchange via cross-attention during the reverse diffusion process. This design enables both tasks to iteratively improve each other. The DDIM backbone ensures high-quality output and efficient inference through deterministic sampling. Experiments on multi-modal medical datasets (MRI, CT, ultrasound) show that DiSIINet achieves significant performance improvements compared to sequential or independent enhancement and segmentation approaches. The code is available at: https://github.com/Reconsider80/DiSIINet.
With the rising demand for trustworthy AI in clinical practice, strong interpretability is now a critical requirement as well as accuracy. However, the modality gap for medical visual question answering is quite severe when continuous visual signals are forcibly projected into discrete text space for reasoning, and the loss of necessary diagnostic information leads to low precision and black-box opacity. To address this problem, we propose MedVCoT, which incorporates latent visual reasoning into the medical visual question answering(VQA) domain. Rather than merely integrating modules, MedVCoT utilizes the specialized expertise of MedSAM to train a large vision-language model so that it can autonomously generate consistent and continuous latent visual tokens within Visual Chain-of-Thought. This mechanism forces the model to explicitly "see" the lesion in the latent space before formulating a textual diagnosis, ensuring answers are causally rooted in verifiable visual evidence rather than statistical hallucination. We achieve this through a progressive 3-stage training procedure: medical feature alignment, visual reasoning learning by utilizing latent tokens generated, and instruction tuning for complex clinical scenarios. Extensive experiments show that MedVCoT can achieve state-of-the-art performance on multiple benchmarks, outperforming other methods by large margins. Meanwhile, it provides pixel-level segmentation masks to validate its diagnostic reasoning. Our demo is available at https://zhuqh19.github.io/MedVCoT.
Continual test-time adaptation (CTTA) adapts a pre-trained medical segmentation model online to an unlabeled target stream whose distribution changes over time. However, most existing CTTA methods rely on pseudo-labeling and self-supervised objectives, which inevitably yield noisy supervision under domain shifts. To mitigate this limitation, we introduce off-the-shelf Vision Foundation Models (VFMs) as external knowledge sources. Zero-shot VFMs are insufficient for medical segmentation because they lack medical semantics, yet they contain rich and heterogeneous generic knowledge. To exploit such external knowledge, we propose Reciprocal Distillation with a Frozen Foundation Model (ReDi-FM), a novel framework with two core components: using structure-aware prompts from the source model to guide the frozen VFM to generate target-adapted supervision, and distilling the resulting knowledge back into the adapting model. For robust distillation, we introduce two complementary objectives: uncertainty-driven hard distillation for precise guidance in ambiguous regions, and hard class-balanced soft distillation for richer supervision of under-represented and challenging structures. A consensus-aware gate further stabilizes adaptation when the two teachers disagree. Extensive experiments on multi-domain medical segmentation benchmarks demonstrate that ReDi-FM outperforms state-of-the-art CTTA methods. Code is available at https://github.com/M4cheal/ReDi-FM.
Network Traffic Anomaly Detection (NTAD), particularly under zero-positive settings, is a critical task in cybersecurity. Existing zero-positive NTAD approaches primarily rely on reconstruction-based pipelines. Nevertheless, these methods are susceptible to an identical shortcut issue, where models indiscriminately reconstruct both normal and anomalous inputs, leading to anomaly overgeneralization. To address this limitation, we propose BiPred, the first prediction-based detection paradigm for NTAD. BiPred symmetrically divides each network traffic sample into two segments and reformulates the NTAD task as a bidirectional prediction across these two segments. Unlike reconstruction-based methods, our BiPred not only eliminates shortcut learning by design but also establishes explicit bidirectional contextual dependencies, making it more sensitive to anomalous traffic. Moreover, we develop a novel Residual Scanning Mamba block that encodes multi-view contextual information to support bidirectional prediction. A residual fusion mechanism is proposed for the multi-view scanning Mamba to suppress the accumulation of inter-view redundancy. This design prevents representation degradation and provides multi-view contextual details for prediction. Extensive experiments demonstrate the superiority of our BiPred paradigm, highlighting a new research direction for NTAD. Code is available at https://github.com/ikun0124/BiPred.
Multi-modal point cloud completion aims to recover complete 3D geometric structures from partial observations by integrating auxiliary data. Although image-guided techniques are well-established, the potential of natural language as a source of high-level semantic cues remains under-explored. Therefore, effectively introducing natural language and synergizing it with the image modality to assist 3D structure recovery is key to breaking current performance bottlenecks. To address this, we propose Query-Aware Gating Gramian Alignment (QGGA), a framework for multi-modal completion. QGGA employs a novel query-dependent bidirectional attention mechanism to dynamically filter information flows between modalities and adaptively regulate fusion intensity. To facilitate alignment among the three heterogeneous modalities, we utilize the volume of the parallelotope spanned by multi-modal local features as a consistency metric, constraining geometric alignment by minimizing this volume. Finally, the fused and aligned features are fed into a coarse-to-fine decoder to first predict a coarse point cloud and then progressively recover the complete shape through stepwise upsampling and refinement. Furthermore, we construct ShapeNet-ViPC-Desc, a benchmark dataset enriched with detailed text descriptions. Experimental results demonstrate that QGGA achieves new state-of-the-art (SOTA) performance on this benchmark. The source code is freely accessible at https://github.com/whysq/QGGA.
Continual unsupervised domain adaptation (UDA) for multi-domain cardiac image segmentation is crucial for clinical deployment under strict privacy constraints, where data from different centers cannot be stored or revisited. However, existing methods still suffer from catastrophic forgetting and training instability caused by noisy pseudo-labels and representation drift. In this study, we propose a novel continual UDA framework that enforces dual-level alignment at the input and feature levels. At the input level, the Domain-Style Adapting Generative Replay (DSA-GR) module employs trajectory-consistent diffusion distillation and style adaptation to synthesize high-fidelity, domain-consistent replay images without storing raw data. At the feature level, the Cross-domain Prototype-guided Feature Consolidation (CPFC) mechanism mitigates representation drift by maintaining a cross-domain prototype memory bank that aggregates features from the current and replay streams. These prototypes act as robust anchors for prototype-guided contrastive learning, rectifying the feature space and suppressing pseudo-label drift. Extensive experiments on two public datasets demonstrate that our framework consistently outperforms state-of-the-art methods. Code will be released at https://github.com/hlyf-xs/CUDA_Seg.
Monocular 3D object detection is a cost-efficient alternative to multisensor systems, yet it remains fragile to multi-component adversarial attacks that perturb the image and tamper with camera calibration. Compounded distortions degrade 3D reasoning by disrupting the correspondence between the 3D geometry and 2D image plane. To address this problem, this work proposes MonoPure, a monocular 3D object detection framework that performs multi-component purification via disentangled and projective representations. MonoPure incorporates a disentangled purification and segmentation module that purifies the image data, with a target-region probability map steering diffusion-based purification to focus on task-relevant regions. In addition, MonoPure presents a 3D detection decoder that integrates 2D skeleton keypoints as object-level spatial cues, enabling occlusion-robust 3D detection. Finally, a projective calib-purification module restores compromised intrinsics by iteratively minimizing the reprojection error between projected 3D boxes and calibration-invariant 2D detection boxes. The experiments confirm that MonoPure outperforms prior detectors under multi-component attacks and occlusion.
Bi-temporal Semantic Change Detection (SCD) is a fundamental analytical framework for capturing state transitions across various domains, with remote sensing being a key application area in this work. However, the prevailing paradigm of comparing two discrete temporal snapshots suffers from inherent limitations due to temporal discontinuity. These limitations lead to pronounced radiometric inconsistencies and pseudo-change noise in Bi-temporal SCD. To address these, we propose the Pseudo-Temporal Feature Fusion Network (PTF-Net), which reconceptualizes bi-temporal SCD as continuous pseudo-temporal evolution modeling. This novel paradigm facilitates the recovery of the missing temporal context between observation points. At its core lies an innovative Non-Linear Style Interpolation Mechanism, which projects discrete bi-temporal observations onto a smooth latent manifold to simulate plausible surface evolution dynamics. The mechanism synthesizes a sequence of semantically coherent intermediate representations, which inherently bridge the temporal gap and disentangles style variations from semantic changes. To fully exploit this synthesized sequence, we design a dedicated Temporal Dynamics Perception Branch. This branch employs efficient temporal interactions to robustly discriminate structural mutations from transient noise, effectively acting as a semantic filter. Extensive experiments on three public datasets reveal that PTF-Net consistently outperforms state-of-the-art methods, achieving comprehensive superiority across mIoU, Fscd, and SeK metrics.
In intraday stock return forecasting, existing Transformer methods face high computational complexity and do not explicitly handle noisy predictors. We propose TransAlpha, a light-weight Transformer variant tailored for cross section data to advance end-to-end forecasting methods with three novel components (Cross-Sectional Denoising for signal purification, Temporal Attention Gating for trend weighting, and Smart Pooling to avoid information loss) along with a multi-component hybrid loss function. We validate on full A-share market stock data and four key segments (CSI 300/500/1000/2000) with 400 proprietary alpha factors of 15-minute frequencies. TransAlpha outperforms state-of-the-art baselines in predictive power and portfolio profitability, indicating tailored Transformer models have significant practical value in quantitative trade.
Open‑vocabulary video instance segmentation (OV‑VIS) couples spatial‑temporal reasoning with language grounding, yet its adversarial robustness has remained unexplored. We present the Dual-Objective Triggers (DOT), the first transferable attack on OV-VIS that simultaneously exploits the vision–language coupling and temporal coherence. DOT deploys a Dual Semantic Perturbation Module that overlays two complementary triggers: a Semantic Suppression Trigger erases the alignment between the true object and the query, while a Plausible Replacement Trigger steers the tracker toward a phantom trajectory that is visually plausible and text‑consistent. To amplify cross‑model transferability without sacrificing perceptual fidelity, we introduce Phase‑Guided Adversarial Training, which injects perturbations primarily in the phase spectrum while blending amplitudes with clean references. Extensive experiments on four state‑of‑the‑art OV‑VIS implementations demonstrate that DOT reduces mAP by up to 69.3% and raises attack success rate by up to 98%, outperforming the strongest baselines by a factor of 1.6× on average, while maintaining a PSNR of 52.49 dB, thus exposing critical security vulnerabilities and laying a foundation for future research on robust and trustworthy vision–language systems.
Bridging the Objective Gap: A Unified Pre-Training Framework for Few-Shot Medical Image Segmentation
Few-shot medical image segmentation relies on dense, boundary-sensitive prototype matching, yet common pre-training objectives mainly optimize global alignment or reconstruction, creating an objective gap that hurts boundary delineation and increases adaptation cost. This raises the question: how to pre-train representations intrinsically matchable for episodic FSS while requiring minimal adaptation-induced re-organization? We introduce a Drift-Gap diagnostic to quantify intrinsic dense-matching misalignment and adaptation-induced feature drift. Guided by this lens, we propose BOG-PRETRAIN, combining Reliability-Gated Alignment to mitigate noisy report supervision, Semantic-Guided MIM to emphasize boundary-informative regions, and Dual Consistency Regularization to stabilize episodic metric geometry. Across five benchmarks (1/4/16-shot), BOG-PRETRAIN improves mean Dice by +15.5/+15.9/+12.5 points over best priors and reduces mean HD95 by 0.9/4.6/10.1; it achieves the lowest Drift (0.052 vs. 0.155 baseline) and Gap_PT (0.256), with ablations confirming the components' complementarity.
In this paper, we revisit multimodal few-shot 3D point cloud semantic segmentation (FS-PCS), identifying a conflict in "Fuse-then-Refine" paradigms: the "Plasticity-Stability Dilemma." In addition, Contrastive Language-Image Pre-training (CLIP)'s inter-class confusion can result in semantic blindness. To address these issues, we present the Decoupled-experts Arbitration Few-Shot SegNet (DA-FSS), a model that effectively distinguishes between semantic and geometric paths and mutually regularizes their gradients to achieve better generalization. DA-FSS employs the same backbone and pre-trained text encoder as the baseline MultiModal Few-Shot SegNet (MM-FSS) to generate text embeddings, which can increase cost-free modalities' utilization rate and better leverage each modality's information space. To achieve this, we propose a Parallel Expert Refinement module to generate each modal correlation. We also propose a Stacked Arbitration Module (SAM) to perform convolutional fusion and arbitrate correlations for each modality pathway. The Parallel Experts decouple two paths: a Geometric Expert maintains plasticity, and a Semantic Expert ensures stability. They are coordinated via a Decoupled Alignment Module (DAM) that transfers knowledge without propagating confusion. Experiments on popular datasets (S3DIS, ScanNet) demonstrate the superiority of DA-FSS over MM-FSS. Meanwhile, geometric boundaries, completeness, and texture differentiation are all superior to the baseline. The code is available at: https://github.com/MoWenQAQ/DA-FSS/.
We present SafeLens, a lightweight segment-level video moderation system that fuses speech, text, and visual frames to produce hateful content detection for each segment. For every segment, SafeLens returns a structured prediction: label, prediction confidence, reasons for flag, harm categories. The structured predictions are optimized for triage, appeals, and downstream enforcement. The system is modular (pluggable speech, text, and visual processing modules back-ends and a mid-size policy Language Language Model (LLM) agent with parameter-efficient tuning). In the live demo, attendees can upload or select clips, scrub the timeline to flag hateful segments, inspect rationales, and vary the policy LLM agent to benchmark the hateful content moderation performance.
Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting
PDF ↗In this work, we introduce CAT-V (Caption Anything in Video), a training-free framework for fine-grained object-centric video captioning of user-selected instances. CAT-V combines (i) a SAMURAI-based Segmenter for precise object masks across frames, (ii) a TRACE-Uni Temporal Analyzer for event boundary detection and coarse event descriptions, and (iii) an InternVL-2.5 Captioner that, conditioned on spatiotemporal visual prompts and chain-of-thought (CoT) guidance, produces detailed, temporally coherent captions about object attributes, actions, states, interactions, and context. The system supports point, box, and region prompts and maintains temporal sensitivity by tracking object states across segments. In contrast to vanilla video captioning that is overly abstract and dense video captioning that is often terse, CAT-V enables object-level specificity with spatial accuracy and temporal coherence, without additional training data.
We present MemoVision, a digital catalog system that captures semantic, spatial, temporal and interaction information as users move around physical environments using client devices such as smart glasses. The system utilizes open-vocabulary semantic segmentation and 3D scans to store objects-of-interest with comprehensive semantic, spatial, temporal and interaction labels. Our demonstration shows multimodal information query and retrieval capabilities, supporting specific queries about object locations, temporal events and user interactions including eye gaze and hand poses, enabling more contextualized responses compared to current multimodal large language models.
Multi-Agent Path Finding (MAPF) algorithms provide highly optimized solutions for coordinating multiple agents in shared environments, yet their outputs lack explainability to human stakeholders. Existing explanation approaches, such as visual trace segmentation or logic-based reasoning, remain fragmented. In this demo, we present OMEGA, an interactive explanation platform that generates Natural Language (NL) explanations using the novel Multi-Agent Planning Ontology (maPO). Our framework transforms raw MAPF planner execution logs into a semantic knowledge graph, enabling SPARQL-based explanations of collision events, replanning strategies, and efficiency trade-offs. A lightweight web interface allows users to query, visualize, and interpret planner decisions, thereby making MAPF solutions transparent and auditable. We conducted a user study that confirms the ontology-driven explanations are significantly clearer and more preferred than raw logs, underscoring the potential of semantic technologies for explainable multi-agent systems.
We present ARGUS, an end-to-end Argument Mining (AM) tool that exploits Large Language Models (LLMs) to automatically perform all core AM tasks, i.e., Argument Component Segmentation, Classification, Relation Identification, and Relation Classification. Furthermore, ARGUS builds the corresponding argumentation framework (AF) and seamlessly integrates symbolic solvers to compute extensions and perform formal reasoning. ARGUS is designed to ensure broad flexibility and usability, supporting any open-source or commercial LLMs and symbolic solvers, providing a ready-to-use platform for exploring neuro-symbolic approaches to argumentation in both research and practical applications.
Recent advances in Large Language Models (LLMs) have achieved state-of-the-art performance in Automatic Speech Recognition (ASR), surpassing ASR-only systems such as Whisper. However, their application to other speech processing tasks, particularly speaker diarisation (SD), remains underexplored. This work proposes extending existing speech-aware LLM architectures with diarisation-specific training and context-based prompting to enable joint transcription and segmentation of multi-speaker audio. By exploiting the semantic reasoning and multilingual capabilities of pretrained LLMs, the proposed approach aims to improve diarisation accuracy, enhancing accessibility for assistive technologies and real-time captioning applications that rely on accurate speaker-aware transcriptions.
Graph-Theoretic Consistency for Robust and Topology-Aware Semi-Supervised Histopathology Segmentation (Student Abstract)
PDF ↗Semi-supervised semantic segmentation (SSSS) is vital in computational pathology, where dense annotations are costly and limited. Existing methods often rely on pixel-level consistency, which propagates noisy pseudo-labels and produces fragmented or topologically invalid masks. We propose Topology Graph Consistency (TGC), a framework that integrates graph-theoretic constraints by aligning Laplacian spectra, component counts, and adjacency statistics between prediction graphs and references. This enforces global topology and improves segmentation accuracy. Experiments on GlaS and CRAG demonstrate that TGC achieves state-of-the-art performance under 5–10% supervision and significantly narrows the gap to full supervision.