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Found 6 papers, 2 papers with code
EvalAI: Towards Better Evaluation Systems for AI Agents
We introduce EvalAI, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence algorithms (AI) at scale.
Do Explanations make VQA Models more Predictable to a Human?
A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable 'explanations' of their decision process, especially for interactive tasks like Visual Question Answering (VQA).
Fabrik: An Online Collaborative Neural Network Editor
We present Fabrik, an online neural network editor that provides tools to visualize, edit, and share neural networks from within a browser.
Evaluating Visual Conversational Agents via Cooperative Human-AI Games
This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.
It Takes Two to Tango: Towards Theory of AI's Mind
Surprisingly, we find that having access to the model's internal states - its confidence in its top-k predictions, explicit or implicit attention maps which highlight regions in the image (and words in the question) the model is looking at (and listening to) while answering a question about an image - do not help people better predict its behavior.
Visual Dialog
We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content.
Ranked #15 on
Visual Dialog
on VisDial v0.9 val
