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Works302 from public data
- 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.
- Object-Part Attention Model for Fine-Grained Image Classification393
The object-part attention model (OPAM) for weakly supervised fine-grained image classification is proposed, which avoids the heavy labor consumption of labeling and compares with more than ten state-of-the-art methods on four widely-used datasets.
- An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges341
An overview of cross-media retrieval is given, including the concepts, methodologies, major challenges, and open issues, as well as building up the benchmarks, including data sets and experimental results so that researchers can directly adopt the benchmarks to promptly evaluate their proposed methods.
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- CM-GANs290
The proposed CM-GAN approach can use GANs to perform cross-modal common representation learning by which the heterogeneous data can be effectively correlated.
- 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.
- Unsupervised Generative Adversarial Cross-Modal Hashing233
This paper proposes an Unsupervised Generative Adversarial Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for unsupervised representation learning to exploit the underlying manifold structure of cross- modal data.
- CCL: Cross-modal Correlation Learning With Multigrained Fusion by Hierarchical Network227
This paper proposes a cross-modal correlation learning (CCL) approach with multigrained fusion by hierarchical network and compares with 13 state-of-the-art methods on 6 widely-used cross- modal datasets the experimental results show the CCL approach achieves the best performance.
- Cross-media shared representation by hierarchical learning with multiple deep networks196
The cross-media multiple deep network (CMDN) is proposed to exploit the complex cross- media correlation by hierarchical learning and achieves better performance comparing with several state-of-the-art methods on 3 extensively used cross- Media datasets.
- Object-Aware Aggregation With Bidirectional Temporal Graph for Video Captioning195
This paper proposes a new video captioning approach based on object-aware aggregation with bidirectional temporal graph (OA-BTG), which captures detailed temporal dynamics for salient objects in video, and learns discriminative spatio-temporal representations by performing object- aware local feature aggregation on detected object regions.
- Modality-Specific Cross-Modal Similarity Measurement With Recurrent Attention Network156
Modality-specific cross-modal similarity measurement approach is proposed by constructing the independent semantic space for each modality, which adopts an end-to-end framework to directly generate the modality- specific cross- modal similarity without explicit common representation.
- SSDH: Semi-Supervised Deep Hashing for Large Scale Image Retrieval156
This paper proposes a semi-supervised loss to jointly minimize the empirical error on labeled data, as well as the embedding error on both labeled and unlabeled data, which can preserve the semantic similarity and capture the meaningful neighbors on the underlying data structures for effective hashing.
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- SCH-GAN: Semi-Supervised Cross-Modal Hashing by Generative Adversarial Network133
A novel semi-supervised cross-modal hashing approach by generative adversarial network (SCH-GAN) is proposed, in which the generative model tries to select margin examples of one modality from unlabeled data when given a query of another modality.
- SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization132
The proposed SIM-Trans achieves state-of-the-art performance on fine-grained visual categorization benchmarks and is light-weighted and can be plugged into any transformer network and trained end-to-end easily.
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- Heterogeneous Metric Learning with Joint Graph Regularization for Cross-Media Retrieval129
A joint graph regularized heterogeneous metric learning (JGRHML) algorithm, which integrates the structure of different media into a joint graphRegularization, which is effective to explore the semantic relationship hidden across different modalities.
- MHTN: Modal-Adversarial Hybrid Transfer Network for Cross-Modal Retrieval128
A modal-adversarial hybrid transfer network (MHTN) is proposed, which aims to realize knowledge transfer from a single-modal source domain to a cross- modal target domain and learn cross-Modal common representation.
- Multi-Behavior Recommendation with Cascading Graph Convolution Networks127
A novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN), in which the embeddings learned from one behavior are used as the input features for the next behavior's embedding learning after a feature transformation operation.
- Two-Stream Collaborative Learning With Spatial-Temporal Attention for Video Classification127
The two-stream collaborative learning with spatial-temporal attention (TCLSTA) approach achieves the best performance compared with more than 10 state-of-the-art methods and adaptively learns the fusion weights of static and motion streams, thus exploiting the strong complementarity betweenstatic and motion information to improve video classification.
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- Cross-media analysis and reasoning: advances and directions104
By presenting approaches, advances, and future directions in cross-media analysis and reasoning, the goal is to draw more attention to the state-of-the-art advances in the field, and to provide technical insights by discussing the challenges and research directions in these areas.
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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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