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Works175 from public data
- Video Summarization With Attention-Based Encoder–Decoder Networks392
This paper proposes a novel video summarization framework named attentive encoder–decoder networks forVideo summarization (AVS), in which the encoder uses a bidirectional long short-term memory (BiLSTM) to encode the contextual information among the input video frames.
- Consensus-Aware Visual-Semantic Embedding for Image-Text Matching196
A Consensus-aware Visual-Semantic Embedding model is proposed to incorporate the consensus information, namely the commonsense knowledge shared between both modalities, into image-text matching and learns the associations and alignments between image and text based on the exploited consensus.
- Episode-Based Prototype Generating Network for Zero-Shot Learning178
A novel generative model is proposed that synthesizes visual prototypes conditioned on the class semantic prototypes into an adversarial framework paired with a parameter-economic Multi-modal Cross-Entropy Loss to capture the discriminative information.
- Improved prototypical networks for few-Shot learning132
This work proposes an attention-analogous strategy to explore the class distribution information by distributing different weights to samples based on their representativeness and proposes a distance scaling strategy to reduce the intra- class difference while enlarge the inter-class difference.
- Step-Wise Hierarchical Alignment Network for Image-Text Matching122
A step-wise hierarchical alignment network (SHAN) that decomposes image-text matching into multi-step cross-modal reasoning process that supplies the model with more complementary and sufficient semantic clues to understand the hierarchical correlations between image and text.
- Saliency-Guided Attention Network for Image-Sentence Matching114
A Saliency-guided Attention Network (SAN) is introduced that is characterized by building an asymmetrical link between vision and language to efficiently learn a fine-grained cross-modal correlation.
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- Transductive Zero-Shot Learning With a Self-Training Dictionary Approach91
A bidirectional mapping-based semantic relationship modeling scheme that seeks for cross-modal knowledge transfer by simultaneously projecting the image features and label embeddings into a common latent space is proposed.
- Attribute-Guided Network for Cross-Modal Zero-Shot Hashing88
A novel attribute-guided network (AgNet) is proposed, which can perform not only IBIR but also text-based image retrieval (TBIR) and bridges the gap caused by modality heterogeneity and zero-shot setting.
- Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning88
A novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions.
- Asymmetric Cross-Scale Alignment for Text-Based Person Search78
A transformer-based model to extract multi-scale representations, and an asymmetric cross-attention mechanism to dynamically align the cross-modal entities in region/image-phrase scales are presented.
- Memorizing Complementation Network for Few-Shot Class-Incremental Learning77
A Memorizing Complementation Network (MCNet) is proposed to ensemble multiple models that complements the different memorized knowledge with each other in novel tasks in few-shot Class-Incremental Learning.
- Zero-Shot Learning via Latent Space Encoding76
Extensive experimental results on four benchmark datasets clearly demonstrate the superiority of the proposed encoder–decoder approach, namely latent space encoding (LSE), on several ZSL tasks, including traditional ZSL, generalized Z SL, and zero-shot retrieval.
- USER: Unified Semantic Enhancement With Momentum Contrast for Image-Text Retrieval72
A Unified Semantic Enhancement Momentum Contrastive Learning (USER) method for ITR is proposed, which delicately design two simple but effective Global representation based Semantic Enhancement (GSE) modules and incorporates the training mechanism of MoCo into ITR.
- Deep attentive and semantic preserving video summarization67
This work incorporates the ideas of both the encoder-decoder attention and semantic preserving loss in a deep Seq2Seq framework for video summarization to enhance the robustness of the model to outliers and introduce Huber loss to replace the popular mean square error loss.
- GTNet: Generative Transfer Network for Zero-Shot Object Detection65
An IoU-Aware Generative Adversarial Network (IoUGAN) is designed as the feature synthesizer, which can be easily integrated into GTNet, and performs favorably against the state-of-the-art ZSD approaches.
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- Ranking Graph Embedding for Learning to Rerank58
It is shown that introducing ranking information into dimensionality reduction significantly increases the performance of image search reranking.
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- Query-aware sparse coding for web multi-video summarization49
A novel query-aware approach is proposed by formulating the multi-video summarization in a sparse coding framework, where the web images searched by a query are taken as the important preference information to reveal the query intent.
- SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior47
A novel approach is proposed that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model along with complementary multi-modal data and exploiting depth data from LiDAR point clouds to supply additional spatial information.
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- Transductive Zero-Shot Learning With Adaptive Structural Embedding45
Two corresponding methods named Adaptive STructural Embedding (ASTE) and Self-PAced Selective Strategy (SPASS) for visual-semantic embedding and domain adaptation in cross-modality learning and unseen class prediction steps are presented.
- Relevance Preserving Projection and Ranking for Web Image Search Reranking45
An HRPP is a spectral embedding algorithm to transform an original high-dimensional feature space into an intrinsically low-dimensional hypersphere space by preserving the manifold structure and a relevance relationship among the images and an H-Rank is a simple but effective ranking algorithm to sort the images by their distances to the hyperspheres center.
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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 2026-10-14. 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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