The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …

Image and text matching plays a crucial role in bridging the cross-modal gap between vision
and language, and has achieved great progress due to the deep learning. However, the …

With the expansion of the Internet and attractive social media infrastructures, people prefer
to follow the news through these media. Despite the many advantages of these media in the …

Downsampling operations such as max pooling or strided convolution are ubiquitously
utilized in Convolutional Neural Networks (CNNs) to aggregate local features, enlarge …

Recent decades have witnessed rapid development of deep neural networks (DNN). As
DNN learning is becoming more and more important to numerous intelligent system, ranging …

Ultra-fine-grained visual categorization (ultra-FGVC) moves down the taxonomy level to
classify sub-granularity categories of fine-grained objects. This inevitably poses a challenge …

Sketch-based image retrieval (SBIR) utilizes sketches to search for images containing
similar objects or scenes. Due to the proliferation of touch-screen devices, sketching has …

Handwriting recognition is a multi-step process that includes data collection, preprocessing,
feature extraction, and classification in order to create a final prediction. This process …

Discriminative feature embedding is of essential importance in the field of large scale face
recognition. In this paper, we propose an attention-based convolutional neural network …

In recent years, deep learning-based methods have emerged as dominant players in the
field of super-resolution (SR), owing to their exceptional reconstruction performance. The …