Achieving Competitive Play Through Bottom-Up Approach in Semantic Segmentation
E. Pryzant, Q. Deng, B. Mei, E. Shrestha

TL;DR
This paper demonstrates that a bottom-up approach using PuckNet can achieve competitive object detection and segmentation performance, and effectively enable AI to play video games like SuperTuxKart.
Contribution
The paper introduces PuckNet, a fully convolutional neural network that detects extreme points for object recognition, challenging the dominance of top-down methods in vision tasks.
Findings
PuckNet achieves a 36.4% bounding box AP on COCO test-dev.
It outperforms vanilla bounding box methods in Mask AP, reaching 17.6%.
Guided segmentation improves Mask AP to 32.1%.
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…
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Human Pose and Action Recognition
