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- 428
- Unsupervised Person Re-Identification by Soft Multilabel Learning421
The reference agent learning to represent each reference person by a reference agent in a joint embedding is introduced and the unified deep model outperforms the state-of-the-art unsupervised RE-ID methods by clear margins.
- Dynamic Refinement Network for Oriented and Densely Packed Object Detection410
A dynamic refinement network that consists of two novel components, i.e., a feature selection module (FSM) and a dynamic refinement head (DRH) that enables neurons to adjust receptive fields in accordance with the shapes and orientations of target objects, which empowers the model to refine the prediction dynamically in an object-aware manner.
- Asymmetric Co-Teaching for Unsupervised Cross-Domain Person Re-Identification172
An asymmetric co-teaching framework, which resists noisy labels by cooperating two models to select data with possibly clean labels for each other, which can consistently benefit most clustering based methods, and boost the state-of-the-art adaptation accuracy.
- Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network166
The three-dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment.
- Transformer-based Dual Relation Graph for Multi-label Image Recognition120
This paper proposes a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, i.e., structural relation graph and semantic relation graph, which aims to capture long-range correlations from object context by developing a cross-scale transformer-based architecture.
- Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification106
This work proposes a novel approach to viewpoint-aware adaptive label smoothing regularization (VALSR) that assigns the adaptive soft label to feature representation and demonstrates that it outperforms the state-of-the-art supervised Re-ID methods.
- 103
- Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield99
The XGBoost algorithm was shown to be the most accurate algorithm among all the investigated four algorithms for UCS prediction of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan.
- 95
- Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion73
This work proposes to decouple the scene and the motion (DSM) with two simple operations, so that the model attention towards the motion information is better paid and the impact of the scene is weakened while the temporal sensitivity of the network is further enhanced.
- Adversarial Attribute-Image Person Re-identification65
This work conducted extensive experiments on three attribute datasets and demonstrated that the regularized adversarial modelling is so far the most effective method for the attribute-image cross-modality person Re-ID problem.
- Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians63
A novel deep network termed Pedestrian-Interference Suppression Network (PISNet) is presented, which leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query.
- Semi-Supervised Adversarial Monocular Depth Estimation61
This paper proposes a semi-supervised adversarial learning framework that only utilizes a small number of image-depth pairs in conjunction with a large number of easily-available monocular images to achieve high performance.
- 57
- 3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters ≤3 cm using HRCT57
An automatic classification framework based on a 3D convolutional neural network to distinguish different types of lung cancer using computed tomography (CT) data is developed and is a promising tool for the diagnosis and treatment choice decision.
- Automatic script identification in the wild57
A large-scale dataset with a great quantity of natural images and 10 types of widely-used languages is constructed and a deep learning based algorithm is proposed for script identification at word or line levels in natural scenes.
- NOH-NMS51
This work proposes \heatmapname (\heatmapnameshort ), which pinpoints the objects nearby each proposal with a Gaussian distribution, together with \nmsname, which dynamically eases the suppression for the space that might contain other objects with a high likelihood.
- Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield49
Using an ML-based intelligent approach, this study was able to provide alternative elucidations for predicting E with appropriate accuracy and run time at Thar coalfield, Pakistan.
- Transfer Deep Reinforcement Learning-Enabled Energy Management Strategy for Hybrid Tracked Vehicle49
An adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL) and the founded DRL and TL-enabled control policy is capable of enhancing energy efficiency and improving system performance.
- Towards convolutional neural networks compression via global error reconstruction47
A global error reconstruction method termed GER is presented, which firstly leverages an SVD-based low-rank approximation to coarsely compress the parameters in the fully connected layers in a layerwise manner, and jointly optimized in a global perspective via back-propagation.
- MEMS reservoir computing system with stiffness modulation for multi-scene data processing at the edge37
This work presents a novel MEMS RC with stiffness modulation, offering a simplified, efficient approach to integrate sensing and computing, and initiated edge computing, enabling emergent applications in MEMS for local computations.
- A review on control factors of pyrolysis technology for plants containing heavy metals36
Current literatures that emphasize the influencing factors on the metal content in the biochar, liquid and gaseous phases, as well as the potential risk of biochar are reviewed.
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- Deep learning for solving inversion problem of atmospheric refractivity estimation30
A network mapping model is established between the sea clutter and the refractivity parameters of the evaporation duct and the surface based duct, combined with the deep learning to verify the feasibility of deep learning in the inversion problem.
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 2026-10-16. 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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