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07/24/2023
Less is More: Focus Attention for Efficient DETR
DETR-like models have significantly boosted the performance of detectors...
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06/26/2023
ParameterNet: Parameters Are All You Need for Large-scale Visual Pretraining of Mobile Networks
The large-scale visual pretraining has significantly improve the perform...
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06/14/2023
GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image
The extraordinary ability of generative models to generate photographic ...
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06/01/2023
Can Large Pre-trained Models Help Vision Models on Perception Tasks?
The recent upsurge in pre-trained large models (e.g. GPT-4) has swept ac...
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05/29/2023
Multiscale Positive-Unlabeled Detection of AI-Generated Texts
Recent releases of Large Language Models (LLMs), e.g. ChatGPT, are aston...
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05/25/2023
VanillaKD: Revisit the Power of Vanilla Knowledge Distillation from Small Scale to Large Scale
The tremendous success of large models trained on extensive datasets dem...
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05/22/2023
VanillaNet: the Power of Minimalism in Deep Learning
At the heart of foundation models is the philosophy of "more is differen...
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03/09/2023
Masked Image Modeling with Local Multi-Scale Reconstruction
Masked Image Modeling (MIM) achieves outstanding success in self-supervi...
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12/29/2022
BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons
This paper studies the problem of designing compact binary architectures...
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12/20/2022
Redistribution of Weights and Activations for AdderNet Quantization
Adder Neural Network (AdderNet) provides a new way for developing energy...
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12/13/2022
FastMIM: Expediting Masked Image Modeling Pre-training for Vision
The combination of transformers and masked image modeling (MIM) pre-trai...
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11/23/2022
GhostNetV2: Enhance Cheap Operation with Long-Range Attention
Light-weight convolutional neural networks (CNNs) are specially designed...
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08/17/2022
AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets
This paper studies the Binary Neural Networks (BNNs) in which weights an...
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06/01/2022
Vision GNN: An Image is Worth Graph of Nodes
Network architecture plays a key role in the deep learning-based compute...
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04/24/2022
Source-Free Domain Adaptation via Distribution Estimation
Domain Adaptation aims to transfer the knowledge learned from a labeled ...
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04/19/2022
Multimodal Token Fusion for Vision Transformers
Many adaptations of transformers have emerged to address the single-moda...
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03/28/2022
Brain-inspired Multilayer Perceptron with Spiking Neurons
Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field o...
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03/28/2022
MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation
This paper studies the 3D instance segmentation problem, which has a var...
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01/27/2022
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
Neural architecture search (NAS) has shown encouraging results in automa...
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01/10/2022
GhostNets on Heterogeneous Devices via Cheap Operations
Deploying convolutional neural networks (CNNs) on mobile devices is diff...
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01/04/2022
PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture
Transformer networks have achieved great progress for computer vision ta...
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12/27/2021
An Empirical Study of Adder Neural Networks for Object Detection
Adder neural networks (AdderNets) have shown impressive performance on i...
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11/24/2021
An Image Patch is a Wave: Phase-Aware Vision MLP
Different from traditional convolutional neural network (CNN) and vision...
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10/27/2021
A Self-adaptive Weighted Differential Evolution Approach for Large-scale Feature Selection
Recently, many evolutionary computation methods have been developed to s...
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09/20/2021
Learning Versatile Convolution Filters for Efficient Visual Recognition
This paper introduces versatile filters to construct efficient convoluti...
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08/30/2021
Hire-MLP: Vision MLP via Hierarchical Rearrangement
This paper presents Hire-MLP, a simple yet competitive vision MLP archit...
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08/16/2021
Neural Architecture Dilation for Adversarial Robustness
With the tremendous advances in the architecture and scale of convolutio...
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07/31/2021
Greedy Network Enlarging
Recent studies on deep convolutional neural networks present a simple pa...
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07/13/2021
CMT: Convolutional Neural Networks Meet Vision Transformers
Vision transformers have been successfully applied to image recognition ...
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07/03/2021
Efficient Vision Transformers via Fine-Grained Manifold Distillation
This paper studies the model compression problem of vision transformers....
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06/30/2021
Augmented Shortcuts for Vision Transformers
Transformer models have achieved great progress on computer vision tasks...
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06/27/2021
Post-Training Quantization for Vision Transformer
Recently, transformer has achieved remarkable performance on a variety o...
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06/21/2021
Federated Learning with Positive and Unlabeled Data
We study the problem of learning from positive and unlabeled (PU) data i...
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06/05/2021
Dynamic Resolution Network
Deep convolutional neural networks (CNNs) are often of sophisticated des...
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06/05/2021
Patch Slimming for Efficient Vision Transformers
This paper studies the efficiency problem for visual transformers by exc...
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05/12/2021
Winograd Algorithm for AdderNet
Adder neural network (AdderNet) is a new kind of deep model that replace...
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04/19/2021
Coarse-to-Fine Searching for Efficient Generative Adversarial Networks
This paper studies the neural architecture search (NAS) problem for deve...
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04/17/2021
Visual Transformer Pruning
Visual transformer has achieved competitive performance on a variety of ...
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03/26/2021
Distilling Object Detectors via Decoupled Features
Knowledge distillation is a widely used paradigm for inheriting informat...
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03/15/2021
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution
Deep learning based methods, especially convolutional neural networks (C...
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03/10/2021
Manifold Regularized Dynamic Network Pruning
Neural network pruning is an essential approach for reducing the computa...
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03/01/2021
Learning Frequency Domain Approximation for Binary Neural Networks
Binary neural networks (BNNs) represent original full-precision weights ...
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02/27/2021
Transformer in Transformer
Transformer is a type of self-attention-based neural networks originally...
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01/25/2021
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence
Convolutional neural networks (CNN) have been widely used for boosting t...
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01/21/2021
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
Modern single image super-resolution (SISR) system based on convolutiona...
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12/23/2020
A Survey on Visual Transformer
Transformer is a type of deep neural network mainly based on self-attent...
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12/01/2020
Dynamic Feature Pyramid Networks for Object Detection
This paper studies feature pyramid network (FPN), which is a widely used...
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12/01/2020
Pre-Trained Image Processing Transformer
As the computing power of modern hardware is increasing strongly, pre-tr...
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10/21/2020
SCOP: Scientific Control for Reliable Neural Network Pruning
This paper proposes a reliable neural network pruning algorithm by setti...
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10/10/2020
