# Achieving Competitive Play Through Bottom-Up Approach in Semantic Segmentation

Source: [Achieving Competitive Play Through Bottom-Up Approach in Semantic Segmentation](<https://www.sota2.com/research/paper/68fa69721e209131cea221f0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:00:47.140Z

Authors: E. Pryzant, Q. Deng, B. Mei, E. Shrestha

Published: 2021-02-28T23:14:13.000Z

## Abstract

With the renaissance of neural networks, object detection has slowly shifted from a bottom-up recognition problem to a top-down approach. Best in class algorithms enumerate a near-complete list of objects and classify each into object/not object. In this paper, we show that strong performance can still be achieved using a bottom-up approach for vision-based object recognition tasks and achieve competitive video game play. We propose PuckNet, which is used to detect four extreme points (top, left, bottom, and right-most points) and one center point of objects using a fully convolutional neural network. Object detection is then a purely keypoint-based appearance estimation problem, without implicit feature learning or region classification. The method proposed herein performs on-par with the best in class region-based detection methods, with a bounding box AP of 36.4% on COCO test-dev. In addition, the extreme points estimated directly resolve into a rectangular object mask, with a COCO Mask AP of 17.6%, outperforming the Mask AP of vanilla bounding boxes. Guided segmentation of extreme points further improves this to 32.1% Mask AP. We applied the PuckNet vision system to the SuperTuxKart video game to test it's capacity to achieve competitive play in dynamic and co-operative multiplayer environments.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2103.00657>)

[PDF](<https://arxiv.org/pdf/2103.00657.pdf>)