Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks342 from public data
- 509
- 340
- 336
- 209
- Hyperspectral Image Classification in the Presence of Noisy Labels203
Experimental studies show that the RLPA can reduce the level of noisy label and demonstrates the advantages of the proposed method over four major classifiers with a significant margin—the gains in terms of the average overall accuracy, average accuracy, and kappa are impressive.
- 192
- Physical Adversarial Attack Meets Computer Vision: A Decade Survey169
A comprehensive overview of physical adversarial attacks is presented and a proposed evaluation metric, hiPAA, comprises six perspectives: Effectiveness, Stealthiness, Robustness, Practicability, Aesthetics, and Economics.
- 161
- 155
- 149
- 144
- Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes139
This paper proposes a Semantic Guidance and Evaluation Network (SGE-Net) to iteratively update the structural priors and the inpainted image in an interplay framework of semantics extraction and image inpainting, which utilizes semantic segmentation map as guidance in each scale of inPainting.
- 129
- Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification123
A unified formulation, a Cascaded Super-Resolution GAN (CSR-GAN) framework is proposed, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person’s identity.
- Image Inpainting Guided by Coherence Priors of Semantics and Textures118
A multi-scale joint optimization framework is adopted to first model the coherence priors and then accordingly interleaving optimize image inpainting and semantic segmentation in a coarse-to-fine manner and two coherence losses are proposed to constrain the consistency between the semantics and the inpainted image in terms of the overall structure and detailed textures.
- Batch mode active sampling based on marginal probability distribution matching114
A novel criterion is proposed which achieves good generalization performance of a classifier by specifically selecting a set of query samples that minimizes the difference in distribution between the labeled and the unlabeled data, after annotation.
- 109
- Magic ELF: Image Deraining Meets Association Learning and Transformer102
This paper aims to unify CNN and Transformer to take advantage of their learning merits for image deraining, and introduces degradation prior to help background recovery and presents the association refinement deraining scheme.
- 97
- 88
- Learning Sparse and Identity-Preserved Hidden Attributes for Person Re-Identification82
This work proposes to incrementally generate Deep Hidden Attribute (DHA) based on baseline deep network for newly uncovered annotations based on an auto-encoder model that can be plugged into any deep network to mine latent information in an unsupervised manner.
- Classroom Learning Status Assessment Based on Deep Learning78
A deep learning-based student classroom behavior recognition method, which extracts the key information of the human skeleton from student behavior images and combines a 10-layer deep convolutional neural network (CNN-10) to recognize students’ classroom behavior.
- 77
- Identifying Influenza Viruses with Resequencing Microarrays76
Resequencing microarrays rapidly identify influenza viruses and provide real-time information about circulating and novel viruses that pose a threat to human health.
- MIMR-DGSA: Unsupervised hyperspectral band selection based on information theory and a modified discrete gravitational search algorithm75
A MIMR-DGSA algorithm for band selection is proposed by following the Maximum-Information-Minimum-Redundancy (MIMR) criterion that maximises the information carried by individual features of a subset and minimises redundant information between them.
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 2026-10-12. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.