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Claim this profileWorks169 from public data
- 1,302
- Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification1,011
Through domain adaptation experiment, it is shown that images generated by SPGAN are more suitable for domain adaptation and yield consistent and competitive re-ID accuracy on two large-scale datasets.
- Conformer: Local Features Coupling Global Representations for Visual Recognition827
Experiments show that Conformer, under the comparable parameter complexity, outperforms the visual transformer (DeiT-B) by 2.3% on ImageNet and on MSCOCO, it outperforms ResNet-101 by 3.7% and 3.6% mAPs for object detection and instance segmentation, respectively, demonstrating the great potential to be a general backbone network.
- Prototype Mixture Models for Few-Shot Semantic Segmentation499
Proposed prototype mixture models (PMMs), which correlate diverse image regions with multiple prototypes to enforce the prototype-based semantic representation, incorporate rich channel-wised and spatial semantics from limited support images.
- 467
- SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images363
A large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images, in which 6 classes of 8,929 prohibited items are manually annotated, and proposes an approach named class-balanced hierarchical refinement (CHR) to deal with these difficulties.
- Weakly Supervised Instance Segmentation Using Class Peak Response298
This paper addresses the challenging image-level supervised instance segmentation task by exploiting class peak responses to enable a classification network for instance mask extraction, and reports state-of-the-art results on popular benchmarks, including PASCAL VOC 2012 and MS COCO.
- Oriented Response Networks288
Over multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, it is consistently observed that replacing regular filters with the proposed ARFs leads to significant reduction in the number of network parameters and improvement in classification performance.
- 278
- C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection261
A continuation optimization method is introduced into MIL and thereby creating continuation multiple instance learning (C-MIL), with the intention of alleviating the non-convexity problem in a systematic way.
- Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection258
- Min-Entropy Latent Model for Weakly Supervised Object Detection232
A min-entropy latent model (MELM) is proposed for weakly supervised object detection, unified with feature learning and optimized with a recurrent learning algorithm, which progressively transfers the weak supervision to object locations.
- Adaptive Keyframe Sampling for Long Video Understanding217
Experiments on two long video understanding benchmarks validate that AKS improves video QA accuracy (beyond strong baselines) upon selecting informative keyframes, and reveals the importance of information pre-filtering in video-based MLLMs.
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- A configurable method for multi-style license plate recognition191
A new method for multi-style LP recognition by representing the styles with quantitative parameters, i.e., plate rotation angle, plate line number, character type and format, which will result in a faster and more precise recognition.
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- 172
- Soft Proposal Networks for Weakly Supervised Object Localization152
This paper designs a network component, Soft Proposal (SP), to be plugged into any standard convolutional architecture to introduce the nearly cost-free object proposal, orders of magnitude faster than state-of-the-art methods.
- SRN: Side-Output Residual Network for Object Symmetry Detection in the Wild110
A new benchmark and an end-to-end deep learning approach are presented, opening up a promising direction for symmetry detection in the wild, and the proposed symmetry detection approach, named Side-output Residual Network (SRN), exploits the flow of errors among multiple scales to ease the problems of fitting complex outputs with limited layers.
- 105
- Visual abnormal behavior detection based on trajectory sparse reconstruction analysis100
A novel abnormal behavior detection approach by introducing trajectory sparse reconstruction analysis (SRA), solved by L1-norm minimization, leading to that a few of dictionary samples are used when reconstructing a behavior trajectory, which guarantees that the proposed approach is valid even when the dictionary set is very small.
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- Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR)83
A new approach for visual object tracking based on Sample-Based Adaptive Sparse Representation (AdaSR), which ensures that the tracked object is adaptively and compactly expressed with predefined samples, which is better than those of several representative tracking methods.
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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