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- A Survey on Learning to Hash1,099
This paper presents a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise Similarity preserving, implicit similarity preserve, as well as quantization, and discusses their relations.
- Inter-media hashing for large-scale retrieval from heterogeneous data sources620
A novel inter-media hashing (IMH) model is proposed to explore the correlations among multiple media types from different data sources and tackle the scalability issue, which transforms multimedia data from heterogeneous data sources into a common Hamming space, in which fast search can be easily implemented by XOR and bit-count operations.
- Binary neural networks: A survey597
A comprehensive survey of algorithms proposed for binary neural networks, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error are presented.
- NAIS: Neural Attentive Item Similarity Model for Recommendation569
This work proposes a neural network model named Neural Attentive Item Similarity model (NAIS), which is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems.
- Learning Deep Representations of Appearance and Motion for Anomalous Event Detection550
This work proposes Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations, and introduces a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies.
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- Forward and Backward Information Retention for Accurate Binary Neural Networks412
The proposed Information Retention Network (IR-Net) is the first to investigate both forward and backward processes of binary networks from the unified information perspective, which provides new insight into the mechanism of network binarization.
- Multiple feature hashing for real-time large scale near-duplicate video retrieval333
This paper presents a novel approach - Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR and shows that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.
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- Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering304
This work proposes a new architecture, Positional Self-Attention with Coattention (PSAC), which does not require RNNs for video question answering and significantly outperforms the state-of-the-art on three tasks and attains comparable result on the Count task.
- Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder276
This paper proposes a novel unsupervised video hashing framework dubbed SSVH, which is able to capture the temporal nature of videos in an end-to-end learning to hash fashion, and designs a hierarchical binary auto-encoder to model the temporal dependencies in videos with multiple granularities.
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- From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning259
Experimental results on the challenging data sets, microsoft video description and microsoft research video-to-text, show that the proposed MS-RNN approach outperforms the state-of-the-art video captioning benchmarks.
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- Hierarchical LSTMs with Adaptive Attention for Visual Captioning245
A hierarchical LSTM with adaptive attention (hLSTMat) approach for image and video captioning that utilizes the spatial or temporal attention for selecting specific regions or frames to predict the related words, while the adaptive attention is for deciding whether to depend on the visual information or the language context information.
- Prompting for Multi-Modal Tracking215
A novel multi-modal prompt tracker (ProTrack), which can transfer the multi- modal inputs to a single modality by the prompt paradigm, and can achieve high-performance multi- Modal tracking by only altering the inputs, even without any extra training on multi-Modal data.
- Patch-Wise Attack for Fooling Deep Neural Network207
A patch-wise iterative algorithm -- a black-box attack towards mainstream normally trained and defense models, which differs from the existing attack methods manipulating pixel-wise noise, and can be generally integrated to any gradient-based attack methods.
- Binary Generative Adversarial Networks for Image Retrieval206
This paper uses binary generative adversarial networks (BGAN) to embed images to binary codes in an unsupervised way and significantly outperforms existing hashing methods by up to 107% in terms of mAP.
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- Quantization-based hashing: a general framework for scalable image and video retrieval182
Quantization-based Hashing (QBH) is a generic framework which incorporates the advantages of quantization error reduction methods into conventional property preserving hashing methods and can be applied to both unsupervised and supervised hashing methods.
- Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking177
This work proposes a novel Multi-modal Tensor Fusion Network (MTFN) to explicitly learn an accurate image-text similarity function with rank-based tensor fusion rather than seeking a common embedding space for each image- text instance.
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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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