Weakly supervised video anomaly detection (WSAD) aims to localise anomalous events in untrimmed videos using only video-level labels. Existing multiple instance learning (MIL) methods often suffer from poor generalisation to unseen anomaly types, unstable temporal attention, and limited adaptability when only a few labelled examples are available. To address these challenges, we propose a meta-learning framework that combines Model-Agnostic Meta-Learning (MAML) with a dual-memory, transformer-based architecture. The model incorporates a dual-branch temporal attention module that captures both long-range semantic dependencies and local temporal proximity, separate memory banks for normal and abnormal prototypes with gated inhibition, metric-learning constraints, and variational latent regularisation. MAML explicitly trains the model for rapid adaptation across heterogeneous anomaly distributions, forcing it to acquire task-invariant representations rather than memorising static training statistics. Extensive experiments on two standard benchmarks yield competitive frame-level AUC of 93.60% on XD-Violence and 86.10% on UCF-Crime. One of our main contributions is the demonstration of very good metrics for zero and few-shot cross dataset transfer experiments, using only a handful of weakly labelled videos. We thus provide a useful proof of concept where MAML has been shown to learn generalized anomaly and non-anomaly representations with a transformer based architecture and a dual memory backbone. A t-SNE analysis of the memory prototypes confirms that MAML produces well-separated normal and abnormal clusters, while without meta-learning the memory banks collapse into entangled representations. The model is also shown to be computationally efficient, confirming its practical value for real-world surveillance deployment.
Shradha Mahadev Naik, Suja Palaniswamy, Nicola Conci· Journal of King Saud Univers...· 0 citations
Deep reinforcement learning is emerging as a powerful alternative to traditional inverse kinematics for controlling robotic manipulators. By learning optimal actions through interaction with the environment, it enables adaptable and precise control in complex continuous workspaces, making it suitable for dynamic manipulator operations. This paper proposes an actor–critic deep reinforcement learning framework for manipulator control in a continuous workspace. Conventional inverse kinematics solutions can become computationally complex for high-degree-of-freedom manipulators or complex kinematic structures. Accurately generating joint angles by deriving configuration-specific equations therefore remains a persistent challenge. This study employs deep deterministic policy gradient and twin delayed deep deterministic policy gradient algorithms to directly predict joint angles for target-reaching tasks, eliminating reliance on conventional inverse kinematics formulations. A key contribution is a simple yet effective linear reward function that supports stable convergence in continuous action spaces. The proposed framework is implemented using ROS2 and Gazebo simulation and validated on a custom-built physical manipulator. The work addresses complex equation-based control through reinforcement learning and demonstrates proof of concept on a 3-degree-of-freedom manipulator. Experimental results show high task success rates of 95.77% for the deep deterministic policy gradient algorithm and 94.71% for the twin delayed deep deterministic policy gradient algorithm, with accurate end-effector positioning within 0.01 m. These results confirm the effectiveness of the proposed framework for accurate and reliable manipulator control with reduced model complexity and improved real-world applicability.
Received: 13 September 2025 | Revised: 14 April 2026 | Accepted: 10 June 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
Data are available from the corresponding author upon reasonable request.
Author Contribution Statement
Shivkumar Sankaralingam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization. NippunKumaar Arulmani Angamuthu: Conceptualization, Methodology, Formal analysis, Investigation, Writing – review & editing, Visualization, Supervision. Suja Palaniswamy: Writing – review & editing, Visualization, Project administration, Funding acquisition.
To accomplish better inventory management, a hybrid framework was created, which integrates Machine Learning (ML) and Reinforcement Learning (RL). A Temporal Fusion Transformer (TFT), trained using two years of retail data which included sales, pricing, promotions, weather conditions, and seasonal variations across various stores and products, was used to estimate demand. The forecasting model achieved a Mean Absolute Error (MAE) of 85.8717 and a Root Mean Squared Error (RMSE) of 109.7081, indicating the complexity and variability of the forecasting task across products and stores. The obtained predictions were added to the dataset and fed to a Soft Actor Critic (SAC) agent. An inventory simulation environment was then established that enabled the agent to learn daily ordering decisions without reference to the original Units Ordered values. The agent fine-tuned its behaviour as training progressed, maintaining realistic inventory levels while minimising unnecessary excess per store-product combination. TFT and SAC hyperparameters were optimised using Optuna to achieve improved performance. The system was tested under multiple real-world demand scenarios and demonstrated adaptability across varying demand patterns, where ML was used for demand estimation and RL for optimising the ordering policy.
John Mohan, Suja Palaniswamy· 2026 7th International Confe...· 0 citations
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