Decoupled Sensitivity-Consistency Learning for Weakly Supervised Video Anomaly Detection

• URL: http://arxiv.org/abs/2603.19780v1
• Date: Fri, 20 Mar 2026 09:15:37 GMT
• Title: Decoupled Sensitivity-Consistency Learning for Weakly Supervised Video Anomaly Detection
• Abstract summary: We propose a novel Decoupled Sensitivity-Consistency framework to train two specialized streams.<n>The temporal sensitivity stream adopts an aggressive optimization strategy to capture high-frequency abrupt changes, whereas the semantic consistency stream applies robust constraints to maintain long-term coherence and reduce noise.<n>Their complementary strengths are fused through a collaborative inference mechanism that reduces individual biases and produces balanced predictions.
• Score: 14.851086599818302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent weakly supervised video anomaly detection methods have achieved significant advances by employing unified frameworks for joint optimization. However, this paradigm is limited by a fundamental sensitivity-stability trade-off, as the conflicting objectives for detecting transient and sustained anomalies lead to either fragmented predictions or over-smoothed responses. To address this limitation, we propose DeSC, a novel Decoupled Sensitivity-Consistency framework that trains two specialized streams using distinct optimization strategies. The temporal sensitivity stream adopts an aggressive optimization strategy to capture high-frequency abrupt changes, whereas the semantic consistency stream applies robust constraints to maintain long-term coherence and reduce noise. Their complementary strengths are fused through a collaborative inference mechanism that reduces individual biases and produces balanced predictions. Extensive experiments demonstrate that DeSC establishes new state-of-the-art performance by achieving 89.37% AUC on UCF-Crime (+1.29%) and 87.18% AP on XD-Violence (+2.22%). Code is available at https://github.com/imzht/DeSC.

Related papers

• Taming the Instability: A Robust Second-Order Optimizer for Federated Learning over Non-IID Data [77.19117648492104]
  We present Federated Robust Curvature Optimization (FedRCO), a novel second-order optimization framework designed to improve convergence speed and reduce communication cost.<n>FedRCO addresses these challenges by integrating an efficient approximate curvature with a provable stability mechanism.<n>We show that FedRCO achieves superior robustness against diverse non-IID scenarios while achieving higher accuracy and faster convergence than both state-of-the-art first-order and second-order methods.
  arXiv  Detail & Related papers  (2026-03-30T11:37:46Z)
• JAWS: Enhancing Long-term Rollout of Neural Operators via Spatially-Adaptive Jacobian Regularization [0.0]
  Jacobian-Adaptive Weighting for Stability (JAWS) is a probabilistic regularization strategy designed to mitigate these limitations.<n>By framing operator learning as Maximum A Posteriori (MAP) estimation with spatially heteroscedastic uncertainty, JAWS dynamically modulates the regularization strength based on local physical complexity.<n> Experiments demonstrate that this spatially-adaptive prior serves as an effective spectral pre-conditioner, which reduces the base operator's burden of handling high-frequency instabilities.
  arXiv  Detail & Related papers  (2026-03-04T06:15:09Z)
• Forward Consistency Learning with Gated Context Aggregation for Video Anomaly Detection [17.79982215633934]
  Video anomaly detection (VAD) aims to measure deviations from normal patterns for various events in real-time surveillance systems.<n>Most existing VAD methods rely on large-scale models to pursue extreme accuracy, limiting their feasibility on resource-limited edge devices.<n>We introduce FoGA, a lightweight VAD model that performs Forward consistency learning with Gated context aggregation.
  arXiv  Detail & Related papers  (2026-01-26T04:35:31Z)
• SynCast: Synergizing Contradictions in Precipitation Nowcasting via Diffusion Sequential Preference Optimization [62.958457694151384]
  We introduce preference optimization into precipitation nowcasting for the first time, motivated by the success of reinforcement learning from human feedback in large language models.<n>In the first stage, the framework focuses on reducing FAR, training the model to effectively suppress false alarms.
  arXiv  Detail & Related papers  (2025-10-22T16:11:22Z)
• On the Adversarial Robustness of Learning-based Conformal Novelty Detection [10.58528988397402]
  We study the adversarial robustness of conformal novelty detection using AdaDetect.<n>Our results show that adversarial perturbations can significantly increase the FDR while maintaining high detection power.
  arXiv  Detail & Related papers  (2025-10-01T03:29:11Z)
• Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning [73.40364018029673]
  Continual test-time adaptive object detection (CTTA-OD) aims to online adapt a source pre-trained detector to ever-changing environments.<n>Our motivation stems from the observation that not all learned source features are beneficial.<n>Our method achieves superior adaptation performance while reducing computational overhead by 12% in FLOPs.
  arXiv  Detail & Related papers  (2025-06-03T05:27:56Z)
• LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders [11.01163097340578]
  We propose Lagrangian-d Robust Embeddings (LORE), a novel unsupervised adversarial fine-tuning framework.<n>LORE significantly improves zero-shot adversarial robustness with minimal degradation in clean data accuracy.
  arXiv  Detail & Related papers  (2025-05-24T21:54:52Z)
• Adaptive Gradient Clipping for Robust Federated Learning [8.268485501864939]
  We propose a principled adaptive clipping strategy, Adaptive Robust Clipping (ARC), which dynamically adjusts clipping thresholds based on the input gradients.<n>ARC significantly enhances robustness, particularly in highly heterogeneous and adversarial settings.
  arXiv  Detail & Related papers  (2024-05-23T11:00:31Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality [131.45028999325797]
  We develop a doubly robust off-policy AC (DR-Off-PAC) for discounted MDP. DR-Off-PAC adopts a single timescale structure, in which both actor and critics are updated simultaneously with constant stepsize. We study the finite-time convergence rate and characterize the sample complexity for DR-Off-PAC to attain an $epsilon$-accurate optimal policy.
  arXiv  Detail & Related papers  (2021-02-23T18:56:13Z)
• Reparameterized Variational Divergence Minimization for Stable Imitation [57.06909373038396]
  We study the extent to which variations in the choice of probabilistic divergence may yield more performant ILO algorithms. We contribute a re parameterization trick for adversarial imitation learning to alleviate the challenges of the promising $f$-divergence minimization framework. Empirically, we demonstrate that our design choices allow for ILO algorithms that outperform baseline approaches and more closely match expert performance in low-dimensional continuous-control tasks.
  arXiv  Detail & Related papers  (2020-06-18T19:04:09Z)
• Detached Error Feedback for Distributed SGD with Random Sparsification [98.98236187442258]
  Communication bottleneck has been a critical problem in large-scale deep learning. We propose a new distributed error feedback (DEF) algorithm, which shows better convergence than error feedback for non-efficient distributed problems. We also propose DEFA to accelerate the generalization of DEF, which shows better bounds than DEF.
  arXiv  Detail & Related papers  (2020-04-11T03:50:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.