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- Deep Learning for Generic Object Detection: A Survey2,921
A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
- A review of uncertainty quantification in deep learning: Techniques, applications and challenges2,893
This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning (RL), and outlines a few important applications of UZ methods.
- Deep Learning for 3D Point Clouds: A Survey2,408
This paper presents a comprehensive review of recent progress in deep learning methods for point clouds, covering three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation.
- Pixel Difference Networks for Efficient Edge Detection541
A simple, lightweight yet effective architecture named Pixel Difference Network (PiDiNet) for efficient edge detection that adopts novel pixel difference convolutions that integrate the traditional edge detection operators into the popular convolutional operations in modern CNNs for enhanced performance on the task, which enjoys the best of both worlds.
- Normalization Techniques in Training DNNs: Methodology, Analysis and Application537
A unified picture of the main motivation behind different approaches from the perspective of optimization is provided, and a taxonomy for understanding the similarities and differences between them is presented.
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- From BoW to CNN: Two Decades of Texture Representation for Texture Classification375
A comprehensive survey of advances in texture representation over the last two decades is presented covering different aspects of the research, including benchmark datasets and state of the art results.
- Local binary features for texture classification: Taxonomy and experimental study369
A large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets.
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- Extended local binary patterns for texture classification343
A novel approach for texture classification, generalizing the well-known local binary pattern (LBP) approach, which produces the best classification results on KTHTIPS2b, and results comparable to the state of the art on CUReT.
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- Learning discriminative representations from RGB-D video data307
This paper introduces an adaptive learning methodology to automatically extract (holistic) spatio-temporal features, simultaneously fusing the RGB and depth information, from RGB-D video data for visual recognition tasks with significant advantages compared with state-of-the-art hand-crafted and machine-learned features.
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- Improved Residual Networks for Image and Video Recognition240
The proposed improvements address all three main components of a ResNet: the flow of information through the network layers, the residual building block, and the projection shortcut, and are able to show consistent improvements in accuracy and learning convergence over the baseline.
- Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning237
Two best UQ methods are applied in two classification phases to analyze two well-known skin cancer datasets, preventing one from making overconfident decisions when it comes to diagnosing the disease.
- Deep Learning for Instance Retrieval: A Survey229
A review of recent instance retrieval works that are developed based on deep learning algorithms and techniques, with the survey organized by deep feature extraction, feature embedding and aggregation methods, and network fine-tuning strategies.
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- Iterative Normalization: Beyond Standardization Towards Efficient Whitening199
This work proposes Iterative Normalization (IterNorm), which employs Newton’s iterations for much more efficient whitening, while simultaneously avoiding the eigen-decomposition, and exclusively introduces Stochastic Normalization Disturbance (SND), which measures the inherent stochastic uncertainty of samples when applied to normalization operations.
- RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision186
This work is the first attempt to train multi-view 3D occupancy models only using 2D labels, reducing the dependence on costly 3D occupancy annotations and introduces an Auxiliary Ray method to tackle the issue of sparse viewpoints in autonomous driving scenarios.
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- Deep Video Super-Resolution Using HR Optical Flow Estimation176
This paper proposes an end-to-end video SR network to super-resolve both optical flows and images and shows that the network achieves the state-of-the-art performance.
- Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision159
This paper proposes a label evolution framework named label evolution with single point supervision (LESPS) to progressively expand the point label by leveraging the intermediate predictions of CNNs, and shows that CNNs equipped with LESPS can well recover the target masks from corresponding point labels.
- From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis159
A new Zero-shot learning framework that can synthesise visual features for unseen classes without acquiring real images is proposed and extensive experimental results manifest that the proposed approach significantly improve the state-of-the-art results.
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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