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Found 6 papers, 5 papers with code
Beyond Faithfulness: A Framework to Characterize and Compare Saliency Methods
Saliency methods calculate how important each input feature is to a machine learning model's prediction, and are commonly used to understand model reasoning.
Cascaded Multilingual Audio-Visual Learning from Videos
In this paper, we explore self-supervised audio-visual models that learn from instructional videos.
Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior
Saliency methods -- techniques to identify the importance of input features on a model's output -- are a common step in understanding neural network behavior.
Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos
Multimodal self-supervised learning is getting more and more attention as it allows not only to train large networks without human supervision but also to search and retrieve data across various modalities.
AVLnet: Learning Audio-Visual Language Representations from Instructional Videos
Further, we propose a tri-modal model that jointly processes raw audio, video, and text captions from videos to learn a multi-modal semantic embedding space useful for text-video retrieval.
Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples
Embeddings mapping high-dimensional discrete input to lower-dimensional continuous vector spaces have been widely adopted in machine learning applications as a way to capture domain semantics.
