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cs.CV2026
Seeing Through Distractions: Context-Stable Attribution via the Least Core
Sai Ganesh Nagarajan, Toshinori Yamauchi, Hiroshi Kera
Shapley-value-based explanations are widely used for feature attribution in machine learning. However, in computer vision, a feature may reduce the target-class score when added to…
cs.LG2026
Rethinking Least-Core Computation in Contextual-Distractor Games
Hiroshi Kera, Toshinori Yamauchi, Sai Ganesh Nagarajan
Game-theoretic attribution explains a model by assigning credit to its features or training examples. The least core has attracted interest as an alternative to Shapley-style avera…
cs.CV2026
Zero-Shot Faithful Textual Explanations via Directional-Derivative Influence on Predictions
Toshinori Yamauchi, Hiroshi Kera, Kazuhiko Kawamoto
Zero-shot textual explanations aim to make image classifiers more transparent by probing their internal representations, without relying on task-specific supervision or LVLMs. Howe…