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Found 6 papers, 4 papers with code
A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility
To study VLN with unknown command feasibility, we introduce a new dataset Mobile app Tasks with Iterative Feedback (MoTIF), where the goal is to complete a natural language command in a mobile app.
Effectively Leveraging Attributes for Visual Similarity
This enables our model to identify that two images contain the same attribute, but can have it deemed irrelevant (e. g., due to fine-grained differences between them) and ignored for measuring similarity between the two images.
Mobile App Tasks with Iterative Feedback (MoTIF): Addressing Task Feasibility in Interactive Visual Environments
In recent years, vision-language research has shifted to study tasks which require more complex reasoning, such as interactive question answering, visual common sense reasoning, and question-answer plausibility prediction.
Can AI decrypt fashion jargon for you?
Finally, we trained a deep learning model that can explicitly predict and explain high level fashion concepts in a product image with its low level and domain specific fashion features.
Learning Type-Aware Embeddings for Fashion Compatibility
Outfits in online fashion data are composed of items of many different types (e. g. top, bottom, shoes) that share some stylistic relationship with one another.
Learning Interpretable Musical Compositional Rules and Traces
Throughout music history, theorists have identified and documented interpretable rules that capture the decisions of composers.
