Deep learning (DL) has recently changed the development of intelligent systems and is
widely adopted in many real-life applications. Despite their various benefits and potentials …

Neural network quantization enables the deployment of large models on resource-
constrained devices. Current post-training quantization methods fall short in terms of …

Edge computing is promising to become one of the next hottest topics in artificial intelligence
because it benefits various evolving domains, such as real-time unmanned aerial systems …

Abstract Binary Neural Networks (BNNs) show great promise for real-world embedded
devices. As one of the critical steps to achieve a powerful BNN, the scale factor calculation …

Small object tracking becomes an increasingly important task, which however has been
largely unexplored in computer vision. The great challenges stem from the facts that: 1) …

Traditional neural architecture search (NAS) has a significant impact in computer vision by
automatically designing network architectures for various tasks. In this paper, binarized …

In recent years, object detectors based on convolutional neural networks have been widely
used on remote sensing images. However, the improvement of their detection performance …

Convolutional neural networks (CNNs) can be quantized to reduce the bit-width of their
weights and activations. Pruning is another compression technique where entire structures …

Searching on bipartite graphs serves as a fundamental task for various real-world
applications, such as recommendation systems, database retrieval, and document querying …

Person re-identification (Re-ID) has been promoted by the significant success of
convolutional neural networks (CNNs). However, the application of such CNN-based Re-ID …