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Found 7 papers, 1 papers with code
A Novel Incremental Cross-Modal Hashing Approach
For the second stage, we propose both a non-deep and deep architectures to learn the hash functions effectively.
A Novel Self-Supervised Re-labeling Approach for Training with Noisy Labels
The major driving force behind the immense success of deep learning models is the availability of large datasets along with their clean labels.
Label Prediction Framework for Semi-Supervised Cross-Modal Retrieval
In this work, we propose a novel framework in a semi-supervised setting, which can predict the labels of the unlabeled data using complementary information from different modalities.
Multi-class Novelty Detection Using Mix-up Technique
Our model which is trained to reveal the constituent classes can then be used to determine whether the sample is novel or not.
Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition
We introduce an out-of-distribution detector that determines whether the video features belong to a seen or unseen action category.
Action Recognition In Videos
Out-of-Distribution Detection
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Semi-Supervised Cross-Modal Retrieval with Label Prediction
Due to abundance of data from multiple modalities, cross-modal retrieval tasks with image-text, audio-image, etc.
Generalized Semantic Preserving Hashing for N-Label Cross-Modal Retrieval
Different scenarios of cross-modal matching are possible, for example, data from the different modalities can be associated with a single label or multiple labels, and in addition may or may not have one-to-one correspondence.
