Nisp: Pruning networks using neuron importance score propagation
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 …
existing methods prune neurons by only considering the statistics of an individual layer or …
Learning rich features for image manipulation detection
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 …
it pays more attention to tampering artifacts than to image content, which suggests that richer …
A global-local self-adaptive network for drone-view object detection
Directly benefiting from the deep learning methods, object detection has witnessed a great
performance boost in recent years. However, drone-view object detection remains …
performance boost in recent years. However, drone-view object detection remains …
Visual relationship detection with internal and external linguistic knowledge distillation
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 …
the object, and a predicate relating them. We leverage the strong correlations between the …
Modeling visual context is key to augmenting object detection datasets
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 …
important for training visual recognition systems. By artificially increasing the number of …
Mlcvnet: Multi-level context votenet for 3d object detection
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 …
information with the self-attention mechanism and multi-scale feature fusion. Most existing …
Zero-shot object detection
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 …
detect object classes which are not observed during training. We work with a challenging set …
Modeling local geometric structure of 3d point clouds using geo-cnn
Recent advances in deep convolutional neural networks (CNNs) have motivated
researchers to adapt CNNs to directly model points in 3D point clouds. Modeling local …
researchers to adapt CNNs to directly model points in 3D point clouds. Modeling local …
TCANet: Triple context-aware network for weakly supervised object detection in remote sensing images
Weakly supervised object detection (WSOD) in remote sensing images (RSI) plays an
essential role in RSI understanding applications. Currently, predominant works are inclined …
essential role in RSI understanding applications. Currently, predominant works are inclined …
Dynamic zoom-in network for fast object detection in large images
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 …
while retaining accuracy for scenarios where objects with varied sizes appear in high …