Existing weakly supervised video violence detection (VVD) methods primarily rely on Euclidean representation learning, which often struggles to distinguish visually similar yet semantically distinct events due to limited hierarchical modeling and insufficient ambiguous training samples. To address this challenge, we propose PiercingEye, a novel dual-space learning framework that synergizes Euclidean and hyperbolic geometries to enhance discriminative feature representation. Specifically, PiercingEye introduces a layer-sensitive hyperbolic aggregation strategy with hyperbolic Dirichlet energy constraints to progressively model event hierarchies, and a cross-space attention mechanism to facilitate complementary feature interactions between Euclidean and hyperbolic spaces. Furthermore, to mitigate the scarcity of ambiguous samples, we leverage large language models to generate logic-guided ambiguous event descriptions, enabling explicit supervision through a hyperbolic vision-language contrastive loss that prioritizes high-confusion samples via dynamic similarity-aware weighting. Extensive experiments on XD-Violence and UCF-Crime benchmarks demonstrate that PiercingEye achieves state-of-the-art performance, with particularly strong results on a newly curated ambiguous event subset, validating its superior capability in fine-grained violence detection.

While unmanned aerial vehicles (UAVs) offer wide-area, high-altitude coverage for anomaly detection, they face challenges such as dynamic viewpoints, scale variations, and complex scenes. Existing datasets and methods, mainly designed for fixed ground-level views, struggle to adapt to these conditions, leading to significant performance drops in drone-view scenarios.To bridge this gap, we introduce A2Seek (Aerial Anomaly Seek), a large-scale, reasoning-centric benchmark dataset for aerial anomaly understanding. This dataset covers various scenarios and environmental conditions, providing high-resolution real-world aerial videos with detailed annotations, including anomaly categories, frame-level timestamps, region-level bounding boxes, and natural language explanations for causal reasoning. Building on this dataset, we propose A2Seek-R1, a novel reasoning framework that generalizes R1-style strategies to aerial anomaly understanding, enabling a deeper understanding of “Where” anomalies occur and “Why” they happen in aerial frames.To this end, A2Seek-R1 first employs a graph-of-thought (GoT)-guided supervised fine-tuning approach to activate the model's latent reasoning capabilities on A2Seek. Then, we introduce Aerial Group Relative Policy Optimization (A-GRPO) to design rule-based reward functions tailored to aerial scenarios. Furthermore, we propose a novel “seeking” mechanism that simulates UAV flight behavior by directing the model's attention to informative regions.Extensive experiments demonstrate that A2Seek-R1 achieves up to a 22.04\% improvement in AP for prediction accuracy and a 13.9\% gain in mIoU for anomaly localization, exhibiting strong generalization across complex environments and out-of-distribution scenarios. Our dataset and code are released at https://2-mo.github.io/A2Seek/.

Video-based Visible-Infrared Person Re-Identification (VVI-ReID) aims to match pedestrian sequences across modalities by extracting modality-invariant sequence-level features. As a high-level semantic representation, language provides a consistent description of pedestrian characteristics in both infrared and visible modalities. Leveraging the Contrastive Language-Image Pre-training (CLIP) model to generate video-level language prompts and guide the learning of modality-invariant sequence-level features is theoretically feasible. However, the challenge of generating and utilizing modality-shared video-level language prompts to address modality gaps remains a critical problem. To address this problem, we propose a simple yet powerful framework, video-level language-driven VVI-ReID (VLD), which consists of two core modules: invariant-modality language prompting (IMLP) and spatial-temporal prompting (STP). IMLP employs a joint fine-tuning strategy for the visual encoder and the prompt learner to effectively generate modality-shared text prompts and align them with visual features from different modalities in CLIP’s multimodal space, thereby mitigating modality differences. Additionally, STP models spatiotemporal information through two submodules, the spatial-temporal hub (STH) and spatial-temporal aggregation (STA), which further enhance IMLP by incorporating spatiotemporal information into text prompts. The STH aggregates and diffuses spatiotemporal information into the [CLS] token of each frame across the vision transformer (ViT) layers, whereas STA introduces dedicated identity-level loss and specialized multihead attention to ensure that the STH focuses on identity-relevant spatiotemporal feature aggregation. The VLD framework achieves state-of-the-art results on two VVI-ReID benchmarks. On the HITSZ-VCM dataset, it improves the Rank-1 accuracy by 7.3% and mAP by 7.6% (infrared-to-visible) and the Rank-1 accuracy by 10.4% and the mAP accuracy by 9.3% (visible to infrared) and requires only 2 hours of training, 2.39M additional parameters, and 0.12G FLOPs. The code will be released at https://github.com/Visuang/VLD.

Conventional reconstruction-based video anomaly detection (VAD) methods implicitly model normality in latent spaces, which is limited by the generalization ability of latent features. Normalizing Flow (NF)-based methods have been introduced to address this issue, as they explicitly model the distribution of input data and achieve significant performance in VAD. However, existing NF-based methods are confined to Euclidean space, limiting their ability to model action hierarchies. While effective at capturing local joint dynamics and short-term temporal variations, they fail to encode kinematic dependencies and long-term pose evolution, ultimately struggling to discern ambiguous anomalies that deviate minimally from normal motion. In contrast, hyperbolic representation learning, with its ability to model hierarchical and complex relationships among actions, offers a promising solution to enhance the discriminative power between similar skeletal actions. Motivated by this, we propose a novel Dual-Space Normalizing Flow (DSNF) method. Specifically, we design a Dual-Space Parallel Graph Convolutional Network (DSPGCN) that synergistically integrates the strengths of both Euclidean and hyperbolic geometries to simultaneously capture local detail features of poses and intrinsic hierarchical relationships of actions. To enhance the model's focus on discriminative features, we design an Adaptive Weighted Approximation Mass (AWAM) loss that dynamically adjusts weights to impose stronger constraints on regions with low discriminability in the dual space, encouraging the model to focus more on key discriminative features in hyperbolic space that reflect complex relationships between actions. Extensive experiments on public datasets demonstrate the effectiveness and robustness of our method in various VAD scenarios.

In this paper, we propose a novel framework, Modality-Free Violence Detection (MFVD), which captures the causal relationships among multimodal cues and ensures stable performance even in the absence of audio information. Specifically, we design a novel Cross-Modal Causal Attention mechanism (CCA) to deal with modality asynchrony by utilizing relative temporal distance and semantic correlation to obtain causal attention between audio and visual information instead of merely calculating correlation scores between audio and visual features. Moreover, to ensure our framework can work well when the audio modality is missing, we design a Cross-Modal Feature Distillation module (CFD), leveraging the common parts of the fused features obtained from CCA to guide the enhancement of visual features. Experimental results on the XD-Violence dataset demonstrate the superior performance of the proposed method in both vision-only and audio-visual modalities, surpassing state-of-the-art methods for both tasks.

While numerous Video Violence Detection (VVD) methods have focused on representation learning in Euclidean space, they struggle to learn sufficiently discriminative features, leading to weaknesses in recognizing normal events that are visually similar to violent events (i.e., ambiguous violence). In contrast, hyperbolic representation learning, renowned for its ability to model hierarchical and complex relationships between events, has the potential to amplify the discrimination between visually similar events. Inspired by these, we develop a novel Dual-Space Representation Learning (DSRL) method for weakly supervised VVD to utilize the strength of both Euclidean and hyperbolic geometries, capturing the visual features of events while also exploring the intrinsic relations between events, thereby enhancing the discriminative capacity of the features. DSRL employs a novel information aggregation strategy to progressively learn event context in hyperbolic spaces, which selects aggregation nodes through layer-sensitive hyperbolic association degrees constrained by hyperbolic Dirichlet energy. Furthermore, DSRL attempts to break the cyber-balkanization of different spaces, utilizing cross-space attention to facilitate information interactions between Euclidean and hyperbolic space to capture better discriminative features for final violence detection. Comprehensive experiments demonstrate the effectiveness of our proposed DSRL.

Existing video anomaly detection methods typically utilize reconstruction or prediction error to detect anomalies in the current frame. However, these methods cannot predict ex-ante potential anomalies in future frames, which is imperative in real scenes. Inspired by the ex-ante prediction ability of humans, we propose an unsupervised Ex-ante Potential Anomaly Prediction Network (EPAP-Net), which learns to build a semantic pool to memorize the normal semantic patterns of future frames for indirect anomaly prediction. At the training time, the memorized patterns are encouraged to be discriminated through our Semantic Pool Building Module (SPBM) with the novel padding and updating strategies. Moreover, we present a novel Semantic Similarity Loss (SSLoss) at the feature level to maximize the semantic consistency of memorized items and corresponding future frames. Specially, to enhance the value of our work, we design a Multiple Frames Prediction module (MFP) to achieve anomaly prediction in future multiple frames. At the test time, we utilize the trained semantic pool instead of ground truth to evaluate the anomalies of future frames. Besides, to obtain better feature representations for our task, we introduce a novel Channel-selected Shift Encoder (CSE), which shifts channels along the temporal dimension between the input frames to capture motion information without generating redundant features. Experimental results demonstrate that the proposed EPAP-Net can effectively predict the potential anomalies in future frames and exhibit superior or competitive performance on video anomaly detection.