Moving Object Detection (MOD) can be outsourced to the cloud for computational convenience, in which case the video must be encrypted to protect privacy. Secure video MOD methods designed to perform MOD on encrypted video are still in their infancy. In this paper, we present an attention-based framework for privacy-preserving MOD in compressed videos. On the user side, we adopt selective video encryption for the compressed video, while in the cloud, we extract the Compressed Video entropy-coded Syntax Elements (CVSE) from the encrypted video. Since the extracted CVSE data lacks sufficient motion information and contains noise, we introduce a two-stage training process. In the first phase, we propose a new deep learning-based approach for interpolating CVSE-based motion feature maps, addressing a significant drawback of traditional methods that rely exclusively on empirical interpolation algorithms. In the second stage, we propose a specialized backbone tailored for feature extraction from sparse CVSE data. We then design an attention-based neck that focuses on areas with denser motion and varying sizes of moving objects. Experimental results on two public datasets, VIRAT and DUKE-MTMC, show that our framework achieves state-of-the-art detection performance. Compared to previous secure solutions, the proposed method exhibits more robustness in challenging scenes.

Cortical neurons often establish multiple synaptic contacts with the same postsynaptic neuron. To avoid functional redundancy of these parallel synapses, it is crucial that each synapse exhibits distinct computational properties. Here we model the current to the soma contributed by each synapse as a sigmoidal transmission function of its presynaptic input, with learnable parameters such as amplitude, slope, and threshold. We evaluate the classification capacity of a neuron equipped with such nonlinear parallel synapses, and show that with a small number of parallel synapses per axon, it substantially exceeds that of the Perceptron. Furthermore, the number of correctly classified data points can increase superlinearly as the number of presynaptic axons grows. When training with an unrestricted number of parallel synapses, our model neuron can effectively implement an arbitrary aggregate transmission function for each axon, constrained only by monotonicity. Nevertheless, successful learning in the model neuron often requires only a small number of parallel synapses. We also apply these parallel synapses in a feedforward neural network trained to classify MNIST images, and show that they can increase the test accuracy. This demonstrates that multiple nonlinear synapses per input axon can substantially enhance a neuron's computational power.

Multi-objective multi-task learning aims to boost the performance of all tasks by leveraging their correlation and conflict appropriately. Nevertheless, in real practice, users may have preference for certain tasks, and other tasks simply serve as privileged or auxiliary tasks to assist the training of target tasks. The privileged tasks thus possess less or even no priority in the final task assessment by users. Motivated by this, we propose a privileged multiple descent algorithm to arbitrate the learning of target tasks and privileged tasks. Concretely, we introduce a privileged parameter so that the optimization direction does not necessarily follow the gradient from the privileged tasks, but concentrates more on the target tasks. Besides, we also encourage a priority parameter for the target tasks to control the potential distraction of optimization direction from the privileged tasks. In this way, the optimization direction can be more aggressively determined by weighting the gradients among target and privileged tasks, and thus highlight more the performance of target tasks under the unified multi-task learning context. Extensive experiments on synthetic and real-world datasets indicate that our method can achieve versatile Pareto solutions under varying preference for the target tasks.