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In this report, we present our approach to the EPIC-KITCHENS VISOR HandObject Segmentation Challenge, which focuses on the estimation of the relationbetween the hands and the objects given a single frame as input. TheEPIC-KITCHENS VISOR dataset provides pixel-wise annotations and serves as abenchmark for hand and active object segmentation in egocentric video. Ourapproach combines the baseline method, i.e., Point-based Rendering (PointRend)and the Segment Anything Model (SAM), aiming to enhance the accuracy of handand object segmentation outcomes, while also minimizing instances of misseddetection. We leverage accurate hand segmentation maps obtained from thebaseline method to extract more precise hand and in-contact object segments. Weutilize the class-agnostic segmentation provided by SAM and apply specifichand-crafted constraints to enhance the results. In cases where the baselinemodel misses the detection of hands or objects, we re-train an object detectoron the training set to enhance the detection accuracy. The detected hand andin-contact object bounding boxes are then used as prompts to extract theirrespective segments from the output of SAM. By effectively combining thestrengths of existing methods and applying our refinements, our submissionachieved the 1st place in terms of evaluation criteria in the VISOR HOSChallenge.

Team I2R-VI-FF Technical Report on EPIC-KITCHENS VISOR Hand Object Segmentation Challenge 2023