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Found 7 papers, 5 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 importance map provided by CHM-Corr and observe updated model decisions.
GlitchBench: Can large multimodal models detect video game glitches?
To address this gap, we introduce GlitchBench, a novel benchmark derived from video game quality assurance tasks, to test and evaluate the reasoning capabilities of LMMs.
PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans
Nearest neighbors (NN) are traditionally used to compute final decisions, e. g., in Support Vector Machines or k-NN classifiers, and to provide users with explanations for the model's decision.
Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors
As a result, it is challenging to fully automate game testing.
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy
Via a large-scale, human study on ImageNet and CUB, our correspondence-based explanations are found to be more useful to users than kNN explanations.
CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning
Our approach does not rely on any external information (such as video metadata); it works solely based on the content of the video.
Under the Skin of Foundation NFT Auctions
Non Fungible Tokens (NFTs) have gained a solid foothold within the crypto community, and substantial amounts of money have been allocated to their trades.
