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Claim this profileWorks106 from public data
- Multi-Label Image Recognition With Graph Convolutional Networks1,262
This work proposes a multi-label classification model based on Graph Convolutional Network (GCN), and proposes a novel re-weighted scheme to create an effective label correlation matrix to guide information propagation among the nodes in GCN.
- BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition1,027
A unified Bilateral-Branch Network (BBN) is proposed to take care of both representation learning and classifier learning simultaneously, where each branch does perform its own duty separately.
- Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval464
The selective convolutional descriptor aggregation (SCDA) method is proposed, which is unsupervised, using no image label or bounding box annotation, and achieves comparable retrieval results with the state-of-the-art general image retrieval approaches.
- Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization392
It is proved that selecting useful deep descriptors contributes well to fine-grained image recognition, and a novel Mask-CNN model without the fully connected layers is proposed, which has a small feature dimensionality and efficient inference speed by comparing with other fine- grained approaches.
- Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation357
This work proposes a novel structure-aware convolutional network to implicitly take priors about the structure of human bodies into account during training of the deep network and significantly outperforms the state-of-the-art methods and almost always generates plausible human pose predictions.
- Exploring Categorical Regularization for Domain Adaptive Object Detection354
This work proposes a simple but effective categorical regularization framework for alleviating domain shift mitigation in the domain adaptive object detection problem, and obtains a significant performance gain over original Domain Adaptive Faster R-CNN detectors.
- Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification305
This work proposes a novel hybrid network structure being composed of a supervised contrastive loss to learn image representations and a cross-entropy loss to learning classifiers, where the learning is progressively transited from feature learning to the classifier learning to embody the idea that better features make better classifiers.
- Delving deep into spatial pooling for squeeze-and-excitation networks279
A simple but effective two-stage spatial pooling process: rich descriptor extraction and information fusion is introduced, which aims to obtain a set of diverse deep descriptors that contain more informative cues than global average-pooling.
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- Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks168
This paper collects existing tricks in long-tailed visual recognition and performs extensive and systematic experiments in order to give a detailed experimental guideline and obtain an effective combination of these tricks, and proposes a novel data augmentation approach based on class activation maps for long-tail recognition.
- Piecewise Classifier Mappings: Learning Fine-Grained Learners for Novel Categories With Few Examples159
An end-to-end trainable deep network inspired by the state-of-the-art fine-grained recognition model and is tailored for the FSFG task is proposed, which generates the decision boundary via learning a set of more attainable sub-classifiers in a more parameter-economic way.
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- Distilling Virtual Examples for Long-tailed Recognition136
It is proved that distilling from these virtual examples is equivalent to label distribution learning under certain constraints, and it is shown that when the virtual example distribution becomes flatter than the original input distribution, the under-represented tail classes will receive significant improvements, which is crucial in long-tailed recognition.
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- Unsupervised object discovery and co-localization by deep descriptor transformation110
Deep Descriptor Transformation (DDT) is proposed, a simple yet effective method for evaluating the correlations of descriptors and then obtaining the category-consistent regions, which can accurately locate the common object in a set of unlabeled images, i.e., object co-localization.
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- Deep Bimodal Regression for Apparent Personality Analysis101
The Deep Bimodal Regression framework is proposed and a solution for the Apparent Personality Analysis competition track in the ChaLearn Looking at People challenge in association with ECCV 2016 is come up with.
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- Learning Semantically Enhanced Feature for Fine-Grained Image Classification91
The approach learns fine-grained features by enhancing the semantics of sub-features of a global feature by arranging feature channels of a CNN into different groups through channel permutation.
- Structure-aware human pose estimation with graph convolutional networks91
This paper proposes a novel model, termed Pose Graph Convolutional Network (PGCN), to exploit the structural relationships between body key points to exploit these important relationships for pose estimation, and shows that this method consistently outperforms competing state-of-the-art methods.
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- Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach76
Comprehensive experimental results and analyses on two new benchmark datasets demonstrate that the proposed method achieves superior performance over the competing baseline models and states-of-the-art.
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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 2026-10-15. 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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