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Showing 1 to 3 of 3 for “"Dataset Annotation"”.

  1. Detecting incidents, accelerating dataset annotation, and estimating depth with multi-view invariants

    … growth since the introduction of large datasets, deep neural networks, and modern computing resources. Current algorithms can perform scene understanding, or the ability to understand and interpret the world through visual perception (e.g., images or videos). In this thesis, we push the …

    mit Repository record for Detecting incidents, accelerating dataset annotation, and estimating depth with multi-view invariants (opens in a new tab)

  2. Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques

    Annotating large unlabeled datasets can be a major bottleneck for machine learning applications. We introduce a scheme for inferring labels of unlabeled data at a fraction of the cost of labeling the entire dataset. We refer to the scheme as Bounded Expectation of Label Assignment (BELA). BELA …

    vt Repository record for Bounded Expectation of Label Assignment: Dataset Annotation by Supervised Splitting with Bias-Reduction Techniques (opens in a new tab)

  3. SAMPLS: A prompt engineering approach using Segment-Anything-Model for PLant Science research

    … can reduce the effort and time consumed in dataset annotation, facilitating a semi-automated training process. Our proposed method improved the detection rate of cells and reduced the error rate as compared to state-of-the-art segmentation tools. We also estimated the IoU scores between the …

    vt Repository record for SAMPLS: A prompt engineering approach using Segment-Anything-Model for PLant Science research (opens in a new tab)