Beyond mAP: Re-evaluating and Improving Performance in Instance Segmentation with Semantic Sorting and Contrastive Flow

Top-down instance segmentation methods improve mAP by hedging bets on low-confidence predictions to match a ground truth. Moreover, the query-key paradigm of top-down methods leads to the instance merging problem. An excessive number of duplicate predictions leads to the (over)counting error, and the independence of category and localization branches leads to the naming error. The de-facto mAP metric doesn't capture these errors, as we show that a trivial dithering scheme can simultaneously increase mAP with hedging errors. To this end, we propose two graph-based metrics that quantifies the amount of hedging both inter-and intra-class. We conjecture the source of the hedging problem is due to feature merging and propose a) Contrastive Flow Field to encode contextual differences between instances as a supervisory signal, and b) Semantic Sorting and NMS step to suppress duplicates and incorrectly categorized prediction. Ablations show that our method encodes contextual information better than baselines, and experiments on COCO our method simultaneously reduces merging and hedging errors compared to state-of-the-art instance segmentation methods.