Cycle-Consistent Weakly Supervised Visual Grounding With Individual and Contextual Representations.

Visual grounding, aiming to align image regions with textual queries, is a fundamental task for cross-modal learning. We study the weakly supervised visual grounding, where only image-text pairs at a coarse-grained level are available. Due to the lack of fine-grained correspondence information, existing approaches often encounter matching ambiguity.

Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification.

The traditional Chinese large-flowered chrysanthemum is one of the cultivar groups of chrysanthemum ( × Ramat.) with great morphological variation based on many cultivars. Some experts have established several large-flowered chrysanthemum classification systems by using the method of comparative morphology.

Image Inpainting With Local and Global Refinement.

Image inpainting has made remarkable progress with recent advances in deep learning. Popular networks mainly follow an encoder-decoder architecture (sometimes with skip connections) and possess sufficiently large receptive field, i.e.

Metric learning for image-based flower cultivars identification.

Background: The study of plant phenotype by deep learning has received increased interest in recent years, which impressive progress has been made in the fields of plant breeding. Deep learning extremely relies on a large amount of training data to extract and recognize target features in the field of plant phenotype classification and recognition tasks. However, for some flower cultivars identification tasks with a huge number of cultivars, it is difficult for traditional deep learning methods to achieve better recognition results with limited sample data.