The tracker based on the Siamese network regards tracking tasks as solving a similarity problem between the target template and search area. Using shallow networks and offline training, these trackers perform well in simple scenarios. ...

In this paper, we strive to propose a self-interpretable framework, termed PrimitiveTree, that incorporates deep visual primitives condensed from deep features with a conventional decision tree, bridging the gap between deep features ...

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• A codebook distills semantic-rich visual primitives from the continuous feature space.

Deep neural network models with strong feature extraction capacity are prone to overfitting and fail to adapt quickly to new tasks with few samples. Gradient-based meta-learning approaches can minimize overfitting and adapt to new tasks fast, but ...

Modern deep convolutional neural networks(CNNs) are often designed to be scalable, leading to the model family concept. A model family is a large (possibly infinite) collection of related neural network architectures. The isomorphism ...

• Novel initialization method (IM Init) by utilizing isomorphism of model family.

Siamese trackers have attracted considerable attention in the field of object tracking because of their high precision and speed. However, one of the main disadvantages of Siamese trackers is that their feature extraction network is ...

In contrast to Convolutional Neural Networks (CNNs), Vision Transformers (ViT) cannot capture sequence ordering of input tokens and require position embeddings. As a learnable fixed-dimension vector, the position embedding improves ...

• Position Embeddings are the linear combination of Gabor Filters and Edge Markers.

• An efficient non-uniform motion deblurring network is proposed to introduce edge information to guide image deblurring task.

Non-uniform motion deblurring has been a challenging problem in the field of computer vision. Currently, deep learning-based deblurring methods have made promising achievements. In this paper, we propose a new joint strong edge and ...

Recently, very deep convolution neural network (CNN) has shown strong ability in single image super-resolution (SISR) and has obtained remarkable performance. However, most of the existing CNN-based SISR methods rarely explicitly use the high-...

• High-frequency information will be lost in the restoration process of super-resolution reconstruction.
• By using image contour information to assist super-resolution reconstruction.
• Introducing spatial channel attention to enhance ...

Designing efficient deep neural networks has achieved great interest in image super-resolution (SR). However, exploring diverse network structures is computationally expensive. More importantly, each layer in a network has a distinct role that ...

• A new search space that allows for channel-level architectural changes.
• A new algorithm that automatically finds multiple heterogeneous activation functions.
• The state-of-the-art lightweight and efficient networks for image super-...

Nowadays, various image editing tools are available that can be utilized for manipulating the original images; here copy-move forgery is most common forgery. In copy-move forgery, some part of the original image is copied and pasted ...

To increase the richness of the extracted text modality feature information and deeply explore the semantic similarity between the modalities. In this paper, we propose a novel method, named adaptive weight multi-channel center similar ...

3D point cloud has tremendous potential in many application tasks. However, the huge amount of data limits this potential. To simplify point clouds and improve their downstream application efficiency, this paper proposes AS-Net, an ...

• A new Attention-aware Sampling Module is proposed to get a sampled point cloud.

Recently, numerous sand dust removal algorithms have been proposed. To our best knowledge, however, most methods evaluated their performance in a no-reference way using few selected real-world images from the internet. It is unclear how to ...

• The article proposed a novel sand dust image imaging model, and constructs a large sand-dust image reconstruction benchmark (SIRB).
• Introducing a set of evaluation strategies to provide comprehensive insights into the strengths and ...

Image dehazing methods aim to solve the problem of poor visibility in images due to haze. Techniques proposed for image dehazing in literature focus on image priors, haze lines or data driven statistical models. Variations of the ...

Multiple JPEG compressions leave artifacts in digital images: residual traces that could be exploited in forensics investigations to recover information about the device employed for acquisition or image editing software. In this paper,...