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- A Survey on Vision Transformer4,578
This paper reviews these vision transformer models by categorizing them in different tasks and analyzing their advantages and disadvantages, and takes a brief look at the self-attention mechanism in computer vision, as it is the base component in transformer.
- Pre-Trained Image Processing Transformer2,345
To maximally excavate the capability of transformer, the IPT model is presented to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs and the contrastive learning is introduced for well adapting to different image processing tasks.
- DAFL: Data-Free Learning of Student Networks453
A novel framework for training efficient deep neural networks by exploiting generative adversarial networks (GANs) is proposed, where the pre-trained teacher networks are regarded as a fixed discriminator and the generator is utilized for derivating training samples which can obtain the maximum response on the discriminator.
- Data-Free Learning of Student Networks453
A novel framework for training efficient deep neural networks by exploiting generative adversarial networks (GANs) is proposed, where the pre-trained teacher networks are regarded as a fixed discriminator and the generator is utilized for derivating training samples which can obtain the maximum response on the discriminator.
- AdderNet: Do We Really Need Multiplications in Deep Learning?240
This paper develops a special back-propagation approach for AdderNets by investigating the full-precision gradient, and proposes an adaptive learning rate strategy to enhance the training procedure of Ad DerNets according to the magnitude of each neuron's gradient.
- AdderSR: Towards Energy Efficient Image Super-Resolution100
This paper thoroughly analyzes the relationship between an adder operation and the identity mapping and insert shortcuts to enhance the performance of SR models using adder networks and develops a learnable power activation for adjusting the feature distribution and refining details.
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- Co-Evolutionary Compression for Unpaired Image Translation93
A novel co-evolutionary approach for reducing their memory usage and FLOPs simultaneously and synergistically optimized for investigating the most important convolution filters iteratively is developed.
- Distilling Portable Generative Adversarial Networks for Image Translation89
Qualitative and quantitative analysis by conducting experiments on benchmark datasets demonstrate that the proposed method can learn portable generative models with strong performance.
- A Semi-Supervised Assessor of Neural Architectures75
This paper employs an auto-encoder to discover meaningful representations of neural architectures and introduces a graph convolutional neural network to predict the performance of architectures based on the learned representations and their relation modeled by the graph.
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- Distilling Semantic Priors from SAM to Efficient Image Restoration Models52
This work proposes a general framework to distill SAM's semantic knowledge to boost exiting IR models without interfering with their inference process, and designs a semantic-guided relation (SGR) module for SPD, which ensures semantic feature representation space consistency to fully distill the priors.
- GenDet: Towards Good Generalizations for AI-Generated Image Detection51
This paper addresses the unseen-generator detection problem by considering this task from the perspective of anomaly detection and proposes an adversarial teacher-student discrepancy-aware framework, which has achieved state-of-the-art on public benchmarks.
- CBQ: Cross-Block Quantization for Large Language Models44
CBQ incorporates a coarse-to-fine preprocessing strategy for suppressing weight and activation outliers, coupled with an adaptive LoRA-Rounding technique for precise weight quantization, enabling CBQ to not only handle extreme outliers effectively but also improve overall quantization accuracy.
- Brain-inspired Multilayer Perceptron with Spiking Neurons43
This paper incorporates the machanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs, and proposes a full-precision LIF operation to communicate be-tween patches, including horizontal LIF and vertical LIF in different directions.
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- LegoNet: Efficient Convolutional Neural Networks with Lego Filters39
A split-transform-merge strategy for an efficient convolution by exploiting intermediate Lego feature maps is developed and Inspired by network engineering, it is suggested that an ordinary filter in the neural network can be upgraded to a sophisticated module as well.
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- Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models33
This paper introduces a novel and adaptively adjustable loss function for weak-to-strong supervision, which involves using a weaker model to supervise a stronger one, aiming to enhance the latter's capabilities beyond the former's limits.
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- Species evolution and quality evaluation of four Coptis herbal medicinal materials in Southwest China26
The phylogenetic analysis of four Coptis herbal medicinal materials based on internal transcribed spacer sequences showed that C. chinensis had been considered to be the best herb, not only the high content in single constituent but also in total alkaloids.
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- Transcriptome analyses provide insights into the difference of alkaloids biosynthesis in the Chinese goldthread (Coptis chinensis Franch.) from different biotopes22
Differential analyses of the transcriptome identified four pivotal candidate enzymes, including aspartate aminotransferaseprotein, polyphenol oxidase, primary-amine oxidationase, and tyrosine decarboxylase, were significantly differentially expressed and may be responsible for the difference of alkaloids contents in the accessions from different biotopes.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. 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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