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Found 14 papers, 2 papers with code
Hierarchical Mutual Information Analysis: Towards Multi-view Clustering in The Wild
Multi-view clustering (MVC) can explore common semantics from unsupervised views generated by different sources, and thus has been extensively used in applications of practical computer vision.
Self-supervised Multi-view Clustering in Computer Vision: A Survey
However, as contrastive learning continues to evolve within the field of computer vision, self-supervised learning has also made substantial research progress and is progressively becoming dominant in MVC methods.
Exploring External Knowledge for Accurate modeling of Visual and Language Problems
We apply this methodology to different AI tasks, including machine translation and image captioning and improve the original state-of-the-art models by a large margin.
Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation
Incomplete Multi-View Clustering aims to enhance clustering performance by using data from multiple modalities.
Journalistic Guidelines Aware News Image Captioning
The task of news article image captioning aims to generate descriptive and informative captions for news article images.
ReFormer: The Relational Transformer for Image Captioning
To improve the quality of image captioning, we propose a novel architecture ReFormer -- a RElational transFORMER to generate features with relation information embedded and to explicitly express the pair-wise relationships between objects in the image.
Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation
In this work, we propose a general framework for unsupervised image-to-image translation across multiple domains, which can translate images from domain X to any a domain without requiring direct training between the two domains involved in image translation.
Learning Tuple Compatibility for Conditional OutfitRecommendation
Our contributions include: 1) Designing a Mixed Category Attention Net (MCAN) which integrates both fine-grained and coarse category information into recommendation and learns the compatibility among fashion tuples.
Cultural Vocal Bursts Intensity Prediction
Recommendation Systems
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards
The goal of this work is to develop a novel learning framework for accurate and expressive fashion captioning.
Learning Color Compatibility in Fashion Outfits
Color compatibility is important for evaluating the compatibility of a fashion outfit, yet it was neglected in previous studies.
Learning Continuous-Time Dynamics by Stochastic Differential Networks
Learning continuous-time stochastic dynamics is a fundamental and essential problem in modeling sporadic time series, whose observations are irregular and sparse in both time and dimension.
Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning
In this paper, we propose a novel deep learning model called Task Adaptive Activation Network (TAAN) that can automatically learn the optimal network architecture for MTL.
Latent Part-of-Speech Sequences for Neural Machine Translation
In this work, we introduce a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics, while allowing for effective and efficient inference over the latent space.
Recognizing License Plates in Real-Time
To enable real-time and accurate license plate recognition, in this work, we propose a set of techniques: 1) a contour reconstruction method along with edge-detection to quickly detect the candidate plates; 2) a simple zero-one-alternation scheme to effectively remove the fake top and bottom borders around plates to facilitate more accurate segmentation of characters on plates; 3) a set of techniques to augment the training data, incorporate SIFT features into the CNN network, and exploit transfer learning to obtain the initial parameters for more effective training; and 4) a two-phase verification procedure to determine the correct plate at low cost, a statistical filtering in the plate detection stage to quickly remove unwanted candidates, and the accurate CR results after the CR process to perform further plate verification without additional processing.
