Image-text retrieval aims to capture the semantic correlation between images and texts.
Existing image-text retrieval methods can be roughly categorized into embedding learning …

Weakly-supervised fine-grained visual categorization (FGVC) aims to achieve subclass
classification within the same large class using only label information. Compared to general …

Multi-modal hashing learns compact binary hash codes by collaborating heterogeneous
multi-modal features at both the model training and online retrieval stages to support large …

Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent
representations of heterogeneous modalities, thereby to search similar instances in one …

Recently, image-text matching has been intensively explored to bridge vision and language.
Previous methods explore an inter-modality relationship between an image-text pair from …

Multi-modality ship image retrieval aims to retrieve ship images from a large dataset,
encompassing various modalities, when provided with a query ship image. One of the key …

There exists a heterogeneity gap between multi-modal data, hence it is difficult to directly
measure the similarity between them. A common way to solve the problem is representation …

Recently, intensive attempts have been made to design robust models for fine-grained
visual recognition, most notably are the impressive gains for training with noisy labels by …

Accurately locating the question-related regions in one given image is crucial for visual
question answering (VQA). The current approaches suffer two limitations:(1) Dividing one …