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Found 5 papers, 2 papers with code
Recurrent Image Annotation With Explicit Inter-Label Dependencies
Additionally, it provides a new perspecitve of looking at an unordered set of labels as equivalent to a collection of different permutations (sequences) of those labels, thus naturally aligning with the image annotation task.
Action-based Early Autism Diagnosis Using Contrastive Feature Learning
To address this, we adopt contrastive feature learning in both self supervised and supervised learning frameworks, and show that these can lead to a significant increase in the prediction accuracy of a binary classifier on this task.
Contrastive Learning of Visual-Semantic Embeddings
Contrastive learning is a powerful technique to learn representations that are semantically distinctive and geometrically invariant.
On-the-fly Global Embeddings Using Random Projections for Extreme Multi-label Classification
The goal of eXtreme Multi-label Learning (XML) is to automatically annotate a given data point with the most relevant subset of labels from an extremely large vocabulary of labels (e. g., a million labels).
Relative Parts: Distinctive Parts for Learning Relative Attributes
The notion of relative attributes as introduced by Parikh and Grauman (ICCV, 2011) provides an appealing way of comparing two images based on their visual properties (or attributes) such as "smiling" for face images, "naturalness" for outdoor images, etc.
