22 accepted papers
In the realm of computer vision, effectively handling multi-tasks simultaneously presents a challenge that necessitates innovative solutions. To better address multiple vision problems, we introduce SeTano, an integrated Graph Neural Network (GNN)-based framework. This framework comprises a Dynamic…
Federated learning for pathological whole slide image (WSI) classification allows multiple clients to train a global multiple instance learning (MIL) model without sharing their privacy-sensitive WSIs. To accommodate the non-independent and identically distributed (non-i.i.d.) feature shifts, cross-…
The generation of high-quality stylized handwritten text images is a challenging task in computer vision and artificial intelligence. While advanced approaches using Latent Diffusion Models (LDMs) for generating stylized handwritten text have shown effectiveness, they often struggle with maintaining…
Automated Essay Scoring (AES) plays a crucial role in language assessment. In particular, cross-prompt essay trait scoring provides learners with valuable feedback to improve their writing skills. However, due to the scarcity of prompts, most existing methods overlook critical information, such as c…
Contrastive learning has advantages for non-parallel voice conversion, but the previous conversion results could be better and more preserved. In previous techniques, negative samples were randomly selected in the features vector from different locations. A positive example could not be effectively…
Recently, end-to-end speech synthesis models based on deep learning have made great progress in speech quality, and gradually replaced traditional speech synthesis methods into the mainstream. However, these methods are still challenging to synthesize highly natural speech. In order to solve the abo…
This work proposes the dense adversarial transfer learning based on class-invariance, which is a novel, unsupervised, conditional adversarial domain adaptation approach. The proposed framework concatenates feature maps from the last layer of each backbone’s block to improve transfer learning; these…
In this paper, we introduce a discriminative vector learning method and apply it to single-channel speech separation. First, speech samples are transformed into discriminative vectors using two backbone networks. These vectors are easily separated by simple clustering algorithms. Among them, vectors…
In the last few years, many deep learning (DL) models have been developed to improve the accuracy of speech emotion recognition (SER). However, as SER datasets are generally small and insufficient due to their difficult and expensive collection, the DL models are prone to overfitting, so their perfo…
The time-domain speech separation methods adopting deep learning have obtained impressive performance. However, the computational complexity, model size, and performance are still the challenges for the implementation on real-time low-resource devices. In this paper, we introduce a lightweight yet e…
Mutual learning, the related idea to knowledge distillation, is a group of untrained lightweight networks, which simultaneously learn and share knowledge to perform tasks together during training. In this paper, we propose a novel mutual learning approach, namely selective mutual learning. This is t…
This paper develops a deep learning model, called Encoder-Recurrent Decoder Network (ERDN), which recovers the clear image from a degrade hazy image without using the atmospheric scattering model. The proposed model consists of two key components- an encoder and a decoder. The encoder is constructed…
In this paper we propose speaker characterization using time delay neural networks and long short-term memory neural networks (TDNN-LSTM) speaker embedding. Three types of front-end feature extraction are investigated to find good features for speaker embedding. Three kinds of data augmentation are…
Traditional speech enhancement techniques modify the magnitude of a speech in time-frequency domain, and use the phase of a noisy speech to resynthesize a time domain speech. This work proposes a complex-valued Gaussian process latent variable model (CGPLVM) to enhance directly the complex-valued no…
Matrix factorization (MF) and its extensions have been intensively studied in computer vision and machine learning. In this paper, unsupervised and supervised learning methods based on MF technique on complex domain are introduced. Projective complex matrix factorization (PCMF) and discriminant proj…
Learning a low-dimensional image representation yields effective and efficient face recognition. The use of such a representation helps to weaken the curse of dimensionality. However, the traditional facial representation method is not robust against partial occlusions or variations of expression. T…
This paper proposes a dynamic tracking attention model (DTAM), which mainly comprises a motion attention mechanism, a convolutional neural network (CNN) and long short-term memory (LSTM), to recognize human action in a video sequence. In the motion attention mechanism, the local dynamic tracking is…
This paper presents an image representation approach which is based on matrix factorization in the complex domain and called exemplar-embed complex matrix factorization (EE-CMF). The proposed EE-CMF approach can very effectively improve the performance of facial expression recognition. Moreover, Wir…
Deep neural network (DNN) have become a popular means of separating a target source from a mixed signal. Most of DNN-based methods modify only the magnitude spectrum of the mixture. The phase spectrum is left unchanged, which is inherent in the short-time Fourier transform (STFT) coefficients of the…
Semantic image segmentation is now an exciting area of research owing to its various useful applications in daily life. This paper introduces a hierarchical joint-guided network (HJGN) which is mainly composed of proposed hierarchical joint learning convolutional networks (HJLCNs) and proposed joint…
This work presents a novel approach for audio event recognition. The approach develops a weighted kernel fisher sparse analysis method based on multiple maps. The proposed method consists of maps extraction and kernel weighted Fisher sparse analysis. Two maps are firstly extracted from each audio fi…
In this paper, we discuss the multipitch streaming (MPS) problem for a multi-source audio signal having interweaving pitch contours. We propose two approaches to tackle this challenge, one relates to a feature extracted from the energy levels distributed in multi-channel recordings for better charac…