IBM Research | ICLR Paper: CLEVRER: Collision Events for Video Representation and Reasoning @ibmresearch | Uploaded June 2020 | Updated October 2024, 4 days ago.
For AI to be truly valuable, it needs to start thinking for itself. That is to say, it must be able to not only analyze data, but understand it as well. Researchers at IBM and the MIT-IBM Watson AI Lab continue to focus on addressing this need and coming up with new tools and techniques that outfit AI with characteristics that enable it to “read between the lines” and provide more explainability and transparency in its decisions.
To this end, the MIT-IBM Watson AI Lab collaborated with researchers from MIT CSAIL, Harvard University and Google DeepMind to create the CoLlision Events for Video Representation and Reasoning, or CLEVRER, dataset that for the first time helps AI recognize objects in videos, analyze their movement and reason about their behaviors. CLEVRER enabled us to benchmark the performances of neural networks and neurosymbolic reasoning—a hybrid of neural networks and symbolic programming—using only a fraction of the data required for traditional deep learning systems. We made the CLEVRER dataset available to larger research community to help them test different AI models.
For AI to be truly valuable, it needs to start thinking for itself. That is to say, it must be able to not only analyze data, but understand it as well. Researchers at IBM and the MIT-IBM Watson AI Lab continue to focus on addressing this need and coming up with new tools and techniques that outfit AI with characteristics that enable it to “read between the lines” and provide more explainability and transparency in its decisions.
To this end, the MIT-IBM Watson AI Lab collaborated with researchers from MIT CSAIL, Harvard University and Google DeepMind to create the CoLlision Events for Video Representation and Reasoning, or CLEVRER, dataset that for the first time helps AI recognize objects in videos, analyze their movement and reason about their behaviors. CLEVRER enabled us to benchmark the performances of neural networks and neurosymbolic reasoning—a hybrid of neural networks and symbolic programming—using only a fraction of the data required for traditional deep learning systems. We made the CLEVRER dataset available to larger research community to help them test different AI models.
