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Found 5 papers, 0 papers with code
Discovering Novel Actions in an Open World with Object-Grounded Visual Commonsense Reasoning
Learning to infer labels in an open world, i. e., in an environment where the target ``labels'' are unknown, is an important characteristic for achieving autonomy.
An FPGA-Based Semi-Automated Traffic Control System Using Verilog HDL
Traffic Congestion is one of the severe problems in heavily populated countries like Bangladesh where Automated Traffic Control System needs to be implemented.
IS-GGT: Iterative Scene Graph Generation With Generative Transformers
Current approaches take a generation-by-classification approach where the scene graph is generated through labeling of all possible edges between objects in a scene, which adds computational overhead to the approach.
Iterative Scene Graph Generation with Generative Transformers
Current approaches take a generation-by-classification approach where the scene graph is generated through labeling of all possible edges between objects in a scene, which adds computational overhead to the approach.
Knowledge Guided Learning: Towards Open Domain Egocentric Action Recognition with Zero Supervision
Building upon the compositional representation offered by Grenander's Pattern Theory formalism, we show that attention and commonsense knowledge can be used to enable the self-supervised discovery of novel actions in egocentric videos in an open-world setting, where data from the observed environment (the target domain) is open i. e., the vocabulary is partially known and training examples (both labeled and unlabeled) are not available.
