To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most
existing methods prune neurons by only considering the statistics of an individual layer or …

Image manipulation detection is different from traditional semantic object detection because
it pays more attention to tampering artifacts than to image content, which suggests that richer …

Directly benefiting from the deep learning methods, object detection has witnessed a great
performance boost in recent years. However, drone-view object detection remains …

Understanding the visual relationship between two objects involves identifying the subject,
the object, and a predicate relating them. We leverage the strong correlations between the …

Performing data augmentation for learning deep neural networks is well known to be
important for training visual recognition systems. By artificially increasing the number of …

In this paper, we address the 3D object detection task by capturing multi-level contextual
information with the self-attention mechanism and multi-scale feature fusion. Most existing …

We introduce and tackle the problem of zero-shot object detection (ZSD), which aims to
detect object classes which are not observed during training. We work with a challenging set …

Recent advances in deep convolutional neural networks (CNNs) have motivated
researchers to adapt CNNs to directly model points in 3D point clouds. Modeling local …

Weakly supervised object detection (WSOD) in remote sensing images (RSI) plays an
essential role in RSI understanding applications. Currently, predominant works are inclined …

We introduce a generic framework that reduces the computational cost of object detection
while retaining accuracy for scenarios where objects with varied sizes appear in high …