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Found 3 papers, 1 papers with code
Naturally Supervised 3D Visual Grounding with Language-Regularized Concept Learners
We propose the Language-Regularized Concept Learner (LARC), which uses constraints from language as regularization to significantly improve the accuracy of neuro-symbolic concept learners in the naturally supervised setting.
Redundancy-aware Transformer for Video Question Answering
This paper identifies two kinds of redundancy in the current VideoQA paradigm.
Discovering Spatio-Temporal Rationales for Video Question Answering
We then conduct extensive studies to verify the importance of STR as well as the proposed answer interaction mechanism.
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