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Claim this profileWorks366 from public data
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- HRank: Filter Pruning Using High-Rank Feature Map922
This paper proposes a novel filter pruning method by exploring the High Rank of feature maps (HRank), inspired by the discovery that the average rank of multiple feature maps generated by a single filter is always the same, regardless of the number of image batches CNNs receive.
- 647
- Towards Optimal Structured CNN Pruning via Generative Adversarial Learning585
This paper proposes an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner and effectively solves the optimization problem by generative adversarial learning (GAL), which learns a sparse soft mask in a label-free and an end to end manner.
- Implicit Diffusion Models for Continuous Super-Resolution407
An Implicit Diffusion Model (IDM) for high-fidelity continuous image super-resolution is introduced, which integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework.
- Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks366
This paper proposes a trellis encoder-decoder network (TEDnet) for crowd counting that achieves the best overall performance in terms of both density map quality and counting accuracy, and proposes a new combinatorial loss to enforce similarities in local coherence and spatial correlation between maps.
- Channel Pruning via Automatic Structure Search293
This paper proposes a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i.e., channel number in each layer, rather than selecting "important" channels as previous works did.
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- Accelerating Convolutional Networks via Global & Dynamic Filter Pruning274
This paper proposes a novel global & dynamic pruning (GDP) scheme to prune redundant filters for CNN acceleration that achieves superior performance to accelerate several cutting-edge CNNs on the ILSVRC 2012 benchmark.
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- Memory Attention Networks for Skeleton-Based Action Recognition204
A newly temporal-then-spatial recalibration method named memory attention networks (MANs) is proposed and deploy MANs using the temporal attention recalibrration module (TARM) and spatiotemporal convolution module (STCM) to handle skeleton-based action recognition.
- Fusion-Mamba for Cross-Modality Object Detection190
This work investigates a novel cross-modality fusion approach by associating cross-modal features in a hidden state space based on an improved Mamba with a gating attention mechanism, and establishes a new baseline for cross-modality object detection.
- 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.
- Land-use scene classification using multi-scale completed local binary patterns170
To further improve the representation power of CLBP, a multi-scale CLBP (MS-CLBP) descriptor is proposed to characterize the dominant texture features in multiple resolutions to improve classification accuracy and computational complexity.
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- Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression142
KSE is capable of simultaneously compressing each layer in an efficient way, which is significantly faster compared to previous data-driven feature map pruning methods, and significantly outperforms state-of-the-art methods.
- Directional binary code with application to PolyU near-infrared face database139
A new coding scheme, namely directional binary code (DBC), is proposed for near-infrared face recognition and three protocols are provided to evaluate and compare the proposed DBC method with baseline face recognition methods, including Gabor based Eigenface, Fisherface and LBP on the PolyU-NIRFD database.
- Geometric Reinforcement Learning for Path Planning of UAVs132
Extensive experimental results validate the effectiveness and feasibility of GRL on the navigation of UAVs.
- Multi-modal data Alzheimer’s disease detection based on 3D convolution131
An image fusion method to fuse Magnetic Resonance Images (MRI) with Positron Emission To-mography (PET) images from AD patients is proposed and the experimental results show that the proposed model achieves better results in terms of accuracy, accuracy and specificity.
- Multi-block SSD based on small object detection for UAV railway scene surveillance129
Experimental results show that the proposed multi-block SSD method produces an overall accuracy of 96.6% and obtains an improvement of up to 9.2% compared with the traditional SSD.
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- TASTA: Text‐Assisted Spatial and Temporal Attention Network for Video Question Answering118
A novel VideoQA model called TASTA is proposed, which shows the great potential of explicitly modeling attention and is made to be simple, small, clean, and efficient for clear performance justification and possible easy extension.
- Multinomial Distribution Learning for Effective Neural Architecture Search115
A Multinomial Distribution Learning for extremely effective NAS is proposed, which considers the search space as a joint multinomial distribution, and the optimal network structure is obtained by the operations with the most likely probability in this distribution.
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 2026-10-14. 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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