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- Perceptual Adversarial Networks for Image-to-Image Transformation393
The perceptual adversarial loss is proposed, which undergoes an adversarial training process between the image transformation network and the discriminative network and can be trained alternately to solve image-to-image transformation tasks.
- Evolutionary Generative Adversarial Networks378
A novel GAN framework called evolutionary GANs (E-GANs) is proposed for stable GAN training and improved generative performance, which overcomes the limitations of an individual adversarial training objective and always preserves the well-performing offspring, contributing to progress in, and the success of GAns.
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- A novel deep learning-based method for damage identification of smart building structures311
A novel method based on deep convolutional neural networks to identify and localise damages of building structures equipped with smart control devices that has outstanding generalisation capacity and higher identification accuracy than other commonly used machine learning methods is proposed.
- RKformer: Runge-Kutta Transformer with Random-Connection Attention for Infrared Small Target Detection259
A novel RKformer model with an encoder-decoder structure, where four specifically designed Runge-Kutta transformer (RKT) blocks are stacked sequentially in the encoder, and a novel random-connection attention (RCA) block, which has a reservoir structure to learn sparse attention via random connections during training.
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- Could AI Ethical Anxiety, Perceived Ethical Risks and Ethical Awareness About AI Influence University Students’ Use of Generative AI Products? An Ethical Perspective147
Ethical awareness can positively influence BI, and perceived ethical risks can also increase PER, which can help university students better accept and ethically use generative AI products.
- Recent Advances for Quantum Neural Networks in Generative Learning143
This paper interprets these QGLMs, covering quantum circuit Born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum variational autoencoders, as the quantum extension of classical generative learning models, and explores their intrinsic relations and their fundamental differences.
- Diff-Font: Diffusion Model for Robust One-Shot Font Generation105
Diff-Font is the first work to utilize a diffusion model for font generation tasks, and outperforms prior font generation methods in both high-fidelity font style replication and the generation of intricate characters.
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- A Systematic Survey of Regularization and Normalization in GANs85
A comprehensive survey on the regularization and normalization techniques from different perspectives of GANs training is conducted and a new taxonomy is proposed based on these objectives, which are summarized at https://github.com/iceli1007/GANs-Regularization-Review.
- Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition69
The first attempt to perform textual reasoning based on visual semantics in this paper is made by paralleling GTR to the language model in a segmentation-based STR baseline, which can effectively exploit the visual-linguistic complementarity via mutual learning.
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- Modeling Image Composition for Complex Scene Generation64
A method that achieves state-of-the-art results on challenging (few-shot) layout-to-image generation tasks by accurately modeling textures, structures and relationships contained in a complex scene by the Transformer with Focal Attention (TwFA).
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- Robust Unpaired Image Dehazing via Density and Depth Decomposition63
A robust self-augmented image dehazing framework for haze generation and removal that can be robustly trained on, not only synthetic indoor datasets, but also real outdoor scenes with remarkable improvement on the real-world image dehazing.
- Tag Disentangled Generative Adversarial Network for Object Image Re-rendering63
A principled Tag Disentangled Generative Adversarial Networks (TD-GAN) for re-rendering new images for the object of interest from a single image of it by specifying multiple scene properties by specifyingmultiple scene properties.
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- HandRefiner: Refining Malformed Hands in Generated Images by Diffusion-based Conditional Inpainting56
This paper introduces a lightweight post-processing solution called HandRefiner, which leverages the hand mesh reconstruction model that consistently adheres to the correct number of fingers and hand shape, while also being capable of fitting the desired hand pose in the generated image.
- MRI-based Alzheimer’s disease prediction via distilling the knowledge in multi-modal data55
This is the first study that attempts to improve an MRI-based prediction model by leveraging extra supervision distilled from multi-modal information, and the proposed multi-instance probabilities demonstrate a superior capability of representing the complicated atrophy distributions.
- Exposure Trajectory Recovery From Motion Blur50
Exposure trajectories are defined, which represent the motion information contained in a blurry image and explain the causes of motion blur, and a novel motion offset estimation framework is proposed to model pixel-wise displacements of the latent sharp image at multiple timepoints.
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- Unified Discrete Diffusion for Simultaneous Vision-Language Generation44
This work unify the discrete diffusion process for multimodal signals by proposing a unified transition matrix and design a mutual attention module with fused embedding layer and a unified objective function to emphasise the inter-modal linkages, which are vital for multi-modality generation.
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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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