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- Hong-Jiang ZhangSuggested from co-authorship
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Works63 from public data
- 1,580
- The application of two-level attention models in deep convolutional neural network for fine-grained image classification829
This paper proposes to apply visual attention to fine-grained classification task using deep neural network and achieves the best accuracy under the weakest supervision condition, and is competitive against other methods that rely on additional annotations.
- Semantic Flow for Fast and Accurate Scene Parsing456
This paper proposes a Flow Alignment Module (FAM) to learn Semantic Flow between feature maps of adjacent levels, and broadcast high-level features to high resolution features effectively and efficiently and exhibits superior performance over other real-time methods even on light-weight backbone networks.
- Hard-Aware Deeply Cascaded Embedding314
The novel framework named Hard-Aware Deeply Cascaded Embedding (HDC) is proposed to ensemble a set of models with different complexities in cascaded manner to mine hard examples at multiple levels and outperforms state-of-the-art methods by a large margin.
- Feature-Metric Loss for Self-supervised Learning of Depth and Egomotion285
The proposed feature-metric loss is proposed and defined on feature representation, where the feature representation is also learned in a self-supervised manner and regularized by both first-order and second-order derivatives to constrain the loss landscapes to form proper convergence basins.
- Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification259
A training algorithm is developed that grows a network not only incrementally but also hierarchically, and divided into component models that predict coarse-grained superclasses and those return final prediction within a superclass.
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- Gated Fully Fusion for Semantic Segmentation249
This paper proposes a new architecture, named Gated Fully Fusion(GFF), to selectively fuse features from multiple levels using gates in a fully connected way, and achieves the state of the art results on four challenging scene parsing datasets including Cityscapes, Pascal Context, COCO-stuff and ADE20K.
- Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching212
This paper proposes to directly add constraints to the cost volume by filtering cost volume with unimodal distribution peaked at true disparities and achieves state-of-the-art performance on Scene Flow and two KITTI stereo benchmarks.
- Dual Graph Convolutional Network for Semantic Segmentation.196
The Dual Graph Convolutional Network (DGCNet) models the global context of the input feature by modelling two orthogonal graphs in a single framework, which achieves state-of-the-art results on both Cityscapes and Pascal Context datasets.
- Exploiting the complementary strengths of multi-layer CNN features for image retrieval133
A mapping function is designed to highlight the effectiveness of low-layer similarity, when measuring fine-grained similarity between query image and its nearest neighbors with similar semantic, and can achieve competitive performance on popular retrieval benchmarks.
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- Sfnet: Faster and Accurate Semantic Segmentation Via Semantic Flow70
A Flow Alignment Module (FAM) is proposed to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high-resolution features effectively and efficiently and integrating this FAM to a standard feature pyramid structure exhibits superior performance over other real-time methods.
- Hierarchical semantic image matching using CNN feature pyramid52
This work proposes a hierarchal image matching method using the CNN feature pyramid, named as CNN Flow, which takes advantage of the complementarity of different layers using guidance from higher layer to lower layer, and demonstrates the superiority of CNN Flow in image dense matching under challenging variations.
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- Social Image Search with Diverse Relevance Ranking42
A diverse relevance ranking scheme which is able to simultaneously take relevance and diversity into account and takes advantage of both the content of images and their associated tags.
- UniOcc: Unifying Vision-Centric 3D Occupancy Prediction with Geometric and Semantic Rendering41
This work proposes a novel Unifying Occupancy (UniOcc) prediction method, explicitly imposing spatial geometry constraint and complementing fine-grained semantic supervision through volume ray rendering, and introduces a Depth-aware Teacher Student (DTS) framework to enhance prediction accuracy using unlabeled data.
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- Bag-of-Words Based Deep Neural Network for Image Retrieval39
A bag-of-words based deep neural network is proposed for image retrieval task, which learns high-level image representation and maps images into bag- of-words space and PageRank algorithm is used to further improve the ranking list by considering visual similarity of images for each query.
- Visual tag dictionary: interpreting tags with visual words37
A corpus named visual tag dictionary is introduced, which interprets each tag with visual words and is constructed in a fully automatic way by exploring the tagged image data on the Internet.
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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 2026-10-13. 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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