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Works292 from public data
- RepVGG: Making VGG-style ConvNets Great Again2,658
A simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3 × 3 convolution and ReLU, while the training-time model has a multi-branch topology.
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- Scaling Up Your Kernels to 31×31: Revisiting Large Kernel Design in CNNs1,445
It is demonstrated that using a few large convolutional kernels instead of a stack of small kernels could be a more powerful paradigm, and proposed RepLKNet, a pure CNN architecture whose kernel size is as large as 31×31, in contrast to commonly used 3×3.
- ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks912
Asymmetric Convolution Block (ACB), an architecture-neutral structure as a CNN building block, which uses 1D asymmetric convolutions to strengthen the square convolution kernels, which can improve the performance of various models on CIFAR and ImageNet by a clear margin.
- Rep ViT: Revisiting Mobile CNN From ViT Perspective745
This study incrementally enhance the mobile-friendliness of a standard lightweight CNN, i.e., MobileNetV3, by integrating the efficient architectural designs of lightweight ViTs, and ends up with a new family of pure lightweight CNNs, namely RepViT.
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- Diverse Branch Block: Building a Convolution as an Inception-like Unit519
A universal building block of Convolutional Neural Network (ConvNet) named Diverse Branch Block (DBB), which enhances the representational capacity of a single convolution by combining diverse branches of different scales and complexities to enrich the feature space, including sequences of convolutions, multiscale convolution, and average pooling.
- Latent semantic sparse hashing for cross-modal similarity search484
A novel Latent Semantic Sparse Hashing (LSSH) is proposed to perform cross-modal similarity search by employing Sparse Coding and Matrix Factorization to capture the salient structures of images and learn the latent concepts from text.
- Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation439
The Hypergraph Computation Empowered Semantic Collecting and Scattering (HGC-SCS) framework, which transposes visual feature maps into a semantic space and constructs a hypergraph for high-order message propagation, enables the model to acquire both semantic and structural information, advancing beyond conventional feature-focused learning.
- IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval426
This paper proposes an Iterative Matching with Recurrent Attention Memory method, in which correspondences between images and texts are captured with multiple steps of alignments, and introduces an iterative matching scheme to explore such fine-grained correspondence progressively.
- Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-Tailed Classification348
This paper proposes a novel self-paced knowledge distillation framework, termed Learning From Multiple Experts (LFME), inspired by the observation that networks trained on less imbalanced subsets of the distribution often yield better performances than their jointly-trained counterparts.
- Large-Scale Cross-Modality Search via Collective Matrix Factorization Hashing218
The experimental results demonstrate that CMFH can significantly outperform several state-of-the-art cross-modality Hashing methods, which validates the effectiveness of the proposed CMFH.
- Centripetal SGD for Pruning Very Deep Convolutional Networks With Complicated Structure216
Centripetal SGD (C-SGD), a novel optimization method, which can train several filters to collapse into a single point in the parameter hyperspace, is proposed, which partly solved an open problem of constrained filter pruning on CNNs with complicated structure, where some layers must be pruned following the others.
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- ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting208
ResRep, a novel method for lossless channel pruning, which slims down a CNN by reducing the width (number of output channels) of convolutional layers, is proposed, which is the first to achieve lossless pruning with such a high compression ratio.
- Transfer Learning with Graph Co-Regularization205
This paper proposes a general framework, referred to as Graph Co-Regularized Transfer Learning (GTL), where various matrix factorization models can be incorporated and proposes two novel methods using NMF and NMTF, respectively.
- Affective Image Content Analysis: Two Decades Review and New Perspectives198
This survey comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges – the affective gap, perception subjectivity, and label noise and absence.
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- Unsupervised Deep Hashing via Binary Latent Factor Models for Large-scale Cross-modal Retrieval180
A novel unsupervised deep learning framework for multimodal data search in a self-taught manner via integrating deep learning and matrix factorization with binary latent factor models that can generate unified binary codes by solving a discrete-constrained objective function directly.
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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 2026-10-13. 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.
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