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Found 10 papers, 10 papers with code
Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationships
While interpretability methods identify a model's learned concepts, they overlook the relationships between concepts that make up its abstractions and inform its ability to generalize to new data.
LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity
Vision Transformers (ViTs), with their ability to model long-range dependencies through self-attention mechanisms, have become a standard architecture in computer vision.
DiffusionWorldViewer: Exposing and Broadening the Worldview Reflected by Generative Text-to-Image Models
Generative text-to-image (TTI) models produce high-quality images from short textual descriptions and are widely used in academic and creative domains.
VisText: A Benchmark for Semantically Rich Chart Captioning
Captions that describe or explain charts help improve recall and comprehension of the depicted data and provide a more accessible medium for people with visual disabilities.
Saliency Cards: A Framework to Characterize and Compare Saliency Methods
Moreover, with saliency cards, we are able to analyze the research landscape in a more structured fashion to identify opportunities for new methods and evaluation metrics for unmet user needs.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
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.
