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Found 5 papers, 1 papers with code
Chop & Learn: Recognizing and Generating Object-State Compositions
Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions.
Disentangling Visual Embeddings for Attributes and Objects
We use visual decomposed features to hallucinate embeddings that are representative for the seen and novel compositions to better regularize the learning of our model.
Learning Graphs for Knowledge Transfer With Limited Labels
The standard paradigm is to utilize relationships in the input graph to transfer information using GCNs from training to testing nodes in the graph; for example, the semi-supervised, zero-shot, and few-shot learning setups.
All About Knowledge Graphs for Actions
Current action recognition systems require large amounts of training data for recognizing an action.
Ranked #17 on
Zero-Shot Action Recognition
on Kinetics
Explicit Bias Discovery in Visual Question Answering Models
It is of interest to the community to explicitly discover such biases, both for understanding the behavior of such models, and towards debugging them.
