One of the most promising techniques used in various sciences is deep neural networks
(DNNs). A special type of DNN called a convolutional neural network (CNN) consists of …

Visual representation based on covariance matrix has demonstrates its efficacy for image
classification by characterising the pairwise correlation of different channels in convolutional …

Fine-grained visual classification can be addressed by deep representation learning under
supervision of manually pre-defined targets (eg, one-hot or the Hadamard codes). Such …

3D human pose estimation can be handled by encoding the geometric dependencies
between the body parts and enforcing the kinematic constraints. Recently, Transformer has …

How to extract distinctive features greatly challenges the fine-grained image classification
tasks. In previous models, bilinear pooling has been frequently adopted to address this …

With the progressive development of ubiquitous computing, wearable human activity
recognition is playing an increasingly important role in many fields, such as health …

Bilinear pooling has achieved excellent performance in fine-grained recognition tasks.
Nevertheless, high-dimensional bilinear features suffer from over-fitting and inefficiency. To …

In this paper, we tackle the 3D object representation learning from the perspective of set-to-
set matching. Given two 3D objects, calculating their similarity is formulated as the problem …

Bilinear pooling is a feature aggregation step applied after the convolutional layers of a
deep network and encodes a matrix of local features into a fixed-size bilinear representation …

In fine-grained recognition, classical high-order coding has inherent contradiction between
visual burstiness and feature redundancy, the core of which is the inherent instability of high …