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Works382 from public data
- Learning Texture Transformer Network for Image Super-Resolution966
A novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys in a transformer, respectively, which achieves significant improvements over state-of-the-art approaches on both quantitative and qualitative evaluations.
- Generating Human Motion from Textual Descriptions with Discrete Representations798
A simple and must-known conditional generative framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transformer for human motion generation from textural descriptions shows better performance than competitive approaches, including recent diffusion-based approaches.
- Spatio-Temporal AutoEncoder for Video Anomaly Detection563
A novel model called Spatio-Temporal AutoEncoding (ST AutoEncoder or STAE), which utilizes deep neural networks to learn video representation automatically and extracts features from both spatial and temporal dimensions by performing 3-dimensional convolutions, which enhances the motion feature learning in videos.
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- Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study288
After training with a large dataset, the DCNN VGG-16 model showed great potential in facilitating PTC diagnosis from cytological images and the contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules.
- Natural Image Matting via Guided Contextual Attention198
This work develops a novel end-to-end approach for natural image matting with a guided contextual attention module, which is specifically designed for image matts and can mimic information flow of affinity-based methods and utilize rich features learned by deep neural networks simultaneously.
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- Deep CTR Prediction in Display Advertising154
A novel deep neural network (DNN) based model is introduced that directly predicts the CTR of an image ad based on raw image pixels and other basic features in one step.
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- Modified generalized projective synchronization of a new fractional-order hyperchaotic system and its application to secure communication144
A novel modified generalized projective synchronization (MGPS) is proposed based on the stability theory of the fractional-order system, where the states of the drive and response systems are asymptotically synchronized up to a desired scaling matrix.
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- Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation135
This work introduces the adversarial training for sequence generation under the Adversarial Variational Bayes (AVB) framework, which enables the model to generate high-quality latent variables and employs the contrastive loss.
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- On stability of nonlinear continuous-time neural networks with delays120
The Lyapunov function method is utilized to analyze stability of continuous nonlinear neural networks with delays and obtain some new sufficient conditions ensuring the globally asymptotic stability independent of delays.
- Fragile watermarking scheme for image authentication111
A simple fragile watermarking scheme for image authentication is proposed that is secure, fast, and capable of detecting and localising modification, and is immune of the well-known quantisation attack.
- An Adversarial Approach to Hard Triplet Generation107
This work proposes an adversarial network for Hard Triplet Generation (HTG) to optimize the network ability in distinguishing similar examples of different categories as well as grouping varied examples of the same categories.
- Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation92
This paper introduces a Contourlet Decomposition Module (CDM) that decomposes low-level features with iterative laplacian pyramid and directional filter bank to mine the structural texture knowledge and proposes a Denoised Texture Intensity Equalization Module (DTIEM) to adaptively extract and enhance statistical texture knowledge through heuristics iterative quantization and denoised operation.
- Digital image splicing detection based on approximate run length91
An approximate run length based scheme is proposed to detect image splicing and demonstrates that the proposed approach can achieve a relatively high accuracy with less computational cost and fewer features when compared with other methods.
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- Using deep convolutional neural networks for multi-classification of thyroid tumor by histopathology: a large-scale pilot study88
The results indicate that DCNN models may have potential for facilitating histopathologic thyroid disease diagnosis, and especially VGG-19 achieved satisfactory accuracies on the task of differentiating thyroid tumors by histopathology.
- Hyperchaotic secure communication via generalized function projective synchronization87
Two different hyperchaotic secure communication schemes by using generalized function projective synchronization (GFPS), where the drive and response systems could be synchronized up to a desired scaling function matrix are presented.
- Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study85
Nodules that are 0.5-1.0 cm in size and have microcalcification and a taller shape can be more accurately diagnosed by Inception-v3, and can achieve an excellent diagnostic efficiency.
- Global exponential stability of delayed competitive neural networks with different time scales75
A time delay parameter is introduced into the neural network model to characterize the signal transmission delays in real neural systems and the finite switch speed in the circuit implementations of neural networks, and a new sufficient condition ensuring global exponential stability of the networks is derived.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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