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Works278 from public data
- 658
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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.
- Neighbourhood Watch: Referring Expression Comprehension via Language-Guided Graph Attention Networks290
A graph-based, language-guided attention mechanism that represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches, and enables the comprehension decision to be visualizable and explainable.
- Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder282
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 Captioning261
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.
- Hierarchical LSTMs with Adaptive Attention for Visual Captioning250
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.
- Deep adversarial metric learning for cross-modal retrieval217
A novel Deep Adversarial Metric Learning approach, termed DAML for cross-modal retrieval, which introduces a modality classifier to predict the modality of a transformed feature, which ensures that the transformed features are also statistically indistinguishable.
- 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.
- Quantization-based hashing: a general framework for scalable image and video retrieval192
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.
- Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences187
A novel Spatio-Temporal Graph Reasoning Network (STGRN) is proposed for this task, which builds a spatio-temporal region graph to capture the region relationships with temporal object dynamics, which involves the implicit and explicit spatial subgraphs in each frame and the temporal dynamic subgraph across frames.
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- Love Thy Neighbour: Automatic Animal Behavioural Classification of Acceleration Data Using the K-Nearest Neighbour Algorithm174
A method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN), and envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.
- Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning171
The proposed framework utilizes the temporal attention for selecting specific frames to predict the related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information.
- Template-Based Math Word Problem Solvers with Recursive Neural Networks168
This paper designs a recursive neural network to encode the quantity with Bi-LSTM and self attention, and infer the unknown operator nodes in a bottom-up manner, and establishes the superiority of the new framework as it improves the accuracy by a wide margin in two of the largest datasets.
- Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network148
This work contributes a neural network solution named DAM, short for Deep Attentive Multi-Task model, which is featured with two special designs: a factorized attention network to aggregate the item embeddings in a bundle to obtain the bundle’s representation and a multi-task manner to alleviate the scarcity of user-bundle interactions.
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- Part-Aware Transformer for Generalizable Person Re-identification131
A pure Transformer model is proposed (termed Part-aware Transformer) for DG-ReID by designing a proxy task, named Cross-ID Similarity Learning (CSL), to mine local visual information shared by different IDs, thus alleviating the side effect of domain-specific biases.
- Learning in high-dimensional multimedia data: the state of the art129
A survey of feature transformation, feature selection and feature encoding, three approaches fighting the consequences of the curse of dimensionality on multimedia learning.
- MathDQN: Solving Arithmetic Word Problems via Deep Reinforcement Learning125
This is the first attempt of applying deep reinforcement learning to solve arithmetic word problems and yields remarkable improvement on most of datasets and boosts the average precision among all the benchmark datasets by 15\%.
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- From General to Specific: Informative Scene Graph Generation via Balance Adjustment99
BA-SGG, a simple yet effective SGG framework based on balance adjustment but not the conventional distribution fitting, integrates two components: Semantic Adjustment (SA) and Balanced Predicate Learning (BPL) respectively for adjusting these imbalances.
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 2026-10-12. 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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