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Found 13 papers, 8 papers with code
Allowing humans to interactively guide machines where to look does not always improve human-AI team's classification accuracy
We build CHM-Corr++, an interactive interface for CHM-Corr, enabling users to edit the feature attribution map provided by CHM-Corr and observe updated model decisions.
WiCV@CVPR2023: The Eleventh Women In Computer Vision Workshop at the Annual CVPR Conference
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2023, organized alongside the hybrid CVPR 2023 in Vancouver, Canada.
Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application
Trust is an important factor in people's interactions with AI systems.
UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs
In this work, we propose UFO, a unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations.
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Despite the proliferation of explainable AI (XAI) methods, little is understood about end-users' explainability needs and behaviors around XAI explanations.
Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability
Second, we find that concepts in the probe dataset are often less salient and harder to learn than the classes they claim to explain, calling into question the correctness of the explanations.
ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features
Specifically, we develop a novel explanation framework ELUDE (Explanation via Labelled and Unlabelled DEcomposition) that decomposes a model's prediction into two parts: one that is explainable through a linear combination of the semantic attributes, and another that is dependent on the set of uninterpretable features.
HIVE: Evaluating the Human Interpretability of Visual Explanations
As AI technology is increasingly applied to high-impact, high-risk domains, there have been a number of new methods aimed at making AI models more human interpretable.
Cleaning and Structuring the Label Space of the iMet Collection 2020
The iMet 2020 dataset is a valuable resource in the space of fine-grained art attribution recognition, but we believe it has yet to reach its true potential.
[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
The implementation of most (7 of 10) methods was straightforward, especially after we received additional details from the original authors.
Information-Theoretic Segmentation by Inpainting Error Maximization
We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets.
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Fair Attribute Classification through Latent Space De-biasing
Fairness in visual recognition is becoming a prominent and critical topic of discussion as recognition systems are deployed at scale in the real world.
