Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks144 from public data
- GhostNet: More Features From Cheap Operations4,809
A novel Ghost module is proposed to generate more feature maps from cheap operations based on a set of intrinsic feature maps to generate many ghost feature maps that could fully reveal information underlying intrinsic features.
- A Survey on Vision Transformer4,574
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.
- CMT: Convolutional Neural Networks Meet Vision Transformers992
A new transformer based hybrid network is proposed by taking advantage of transformers to capture long-range dependencies, and of CNNs to extract local information, obtaining much better trade-off for accuracy and efficiency than previous CNN-based and transformer-based models.
- 964
- 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.
- 399
- 331
- Multimodal Token Fusion for Vision Transformers327
The design of TokenFusion allows the transformer to learn correlations among multimodal features, while the single-modal transformer architecture remains largely intact, and surpasses state-of-the-art methods in three typical vision tasks: multimodals image-to-image translation, RGB-depth semantic segmentation, and 3D object detection with point cloud and images.
- Distilling Object Detectors via Decoupled Features278
This paper presents a novel distillation algorithm via decoupled features (DeFeat) for learning a better student detector that is able to surpass the state-of-the-art distillation methods for object detection.
- CARS: Continuous Evolution for Efficient Neural Architecture Search275
This work develops an efficient continuous evolutionary approach for searching neural networks that provides a series of networks with the number of parameters ranging from 3.7M to 5.1M under mobile settings and surpasses those produced by the state-of-the-art methods on the benchmark ImageNet dataset.
- 248
- 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.
- Patch Slimming for Efficient Vision Transformers218
A novel patch slimming approach that discards useless patches in a topdown paradigm is presented that can significantly reduce the computational costs of vision transformers without affecting their performances.
- CNNpack: packing convolutional neural networks in the frequency domain198
This work presents an effective CNN compression approach in the frequency domain, which focuses not only on smaller weights but on all the weights and their underlying connections, and relax the computational burden of convolution operations in CNNs by linearly combining the convolution responses of discrete cosine transform (DCT) bases.
- 183
- An Image Patch is a Wave: Phase-Aware Vision MLP176
Extensive experiments demonstrate that the proposed Wave-MLP is superior to the state-of-the-art MLP architectures on various vision tasks such as image classification, object detection and semantic segmentation.
- Efficient Residual Dense Block Search for Image Super-Resolution152
An efficient residual dense block search algorithm with multiple objectives to hunt for fast, lightweight and accurate networks for image super-resolution models achieves better performance than the state-of-the-art methods with limited number of parameters and FLOPs.
- 140
- Hire-MLP: Vision MLP via Hierarchical Rearrangement128
Hire-MLP is presented, a simple yet competitive vision MLP architecture via Hierarchical rearrangement, which contains two levels of rearrangements to enable information communication between different regions and capture global context by circularly shifting all tokens along spatial directions.
- 114
- Manifold Regularized Dynamic Network Pruning114
A new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks (dubbed as ManiDP) is proposed, which shows better performance in terms of both accuracy and computational cost compared to the state-of-the-art methods.
- Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection110
This work proposes a hierarchical trinity search framework to simultaneously discover efficient architectures for all components of object detector in an end-to-end manner and empirically reveals that different parts of the detector prefer different operators.
- ReNAS: Relativistic Evaluation of Neural Architecture Search107
This paper proposes a relativistic architecture performance predictor in NAS (ReNAS), encoding neural architectures into feature tensors, and further refining the representations with the predictor, to determine which architecture would perform better instead of accurately predict the absolute architecture performance.
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 2026-10-13. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.