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Found 4 papers, 2 papers with code
Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment
In this paper, we propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i. e., both kinematic and visual data, from simulator to real robot.
LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow Recognition
Specifically, we propose a non-local recurrent convolutional network (NL-RCNet), which introduces non-local block to capture the long-range temporal dependency (LRTD) among continuous frames.
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis
In this paper, we present a novel active learning framework for cost-effective skin lesion analysis.
Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification
In this paper, we propose an effective iterative learning framework for noisy-labeled medical image classification, to combat the lacking of high quality annotated medical data.
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