Original title: ChessVision — A Dataset for Logically Coherent Multi-label Classification
Authors: Soumadeep Saha, Utpal Garain
In the realm of computer vision and deep learning, there’s been remarkable progress. But there’s a hitch—these smart systems sometimes miss the bigger picture. They rely on surface details rather than understanding the context or following rules. For critical tasks, this is a big issue. So, to tackle this problem, a new dataset emerges: ChessVision. It’s a treasure trove of over 200,000 images showing chess games in progress. But it’s not just about chess—it’s a challenge. These images come with strict rules. The task? Recreate the chess game from just the picture. These rules force the models to think logically, testing their ability to grasp concepts like position and count. While popular vision models perform well by regular standards, they stumble when it comes to following the game’s logic. ChessVision isn’t just a dataset; it’s a gauntlet thrown down for future improvements in this field.
Original article: https://arxiv.org/abs/2311.12610