Encoding user interactions into visual in-context example pairs turns static models into controllable systems that improve IoU, PSNR, and LPIPS on guided tasks without retraining.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 3representative citing papers
iTeach is a failure-driven interactive teaching system that adapts pretrained robot perception models in the wild via short human-object interactions and few-shot semi-supervised label propagation, yielding improved segmentation and grasping performance.
Face segmentation for background removal systematically impacts both face recognition performance and morphing attack detection in unconstrained scenarios.
citing papers explorer
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From Static to Interactive: Adapting Visual in-Context Learners for User-Driven Tasks
Encoding user interactions into visual in-context example pairs turns static models into controllable systems that improve IoU, PSNR, and LPIPS on guided tasks without retraining.
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iTeach: In the Wild Interactive Teaching for Failure-Driven Adaptation of Robot Perception
iTeach is a failure-driven interactive teaching system that adapts pretrained robot perception models in the wild via short human-object interactions and few-shot semi-supervised label propagation, yielding improved segmentation and grasping performance.
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On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detection
Face segmentation for background removal systematically impacts both face recognition performance and morphing attack detection in unconstrained scenarios.