Call for Participation in Data Challenges

CODS-COMAD Data Challenge is a pre-conference competition. The challenge aims to attract teams from the Data Science, Data mining, and Machine Learning community to participate. We expect students, practitioners, and researchers to form teams and participate in the competition.

1. Data Challenge 1: Predict Key Attributes from Product Images

Have you ever encountered a product listing on an e-commerce platform where the image showed a short-sleeve shirt, but the description claimed it was long-sleeved? Such discrepancies are not just frustrating for customers—they're a significant challenge for e-commerce platforms striving to maintain accurate product catalogs at scale.

Meesho is sponsoring the data challenge that addresses this critical issue in the e-commerce industry. Participants will develop models to automatically predict key product attributes from images, revolutionizing how products are cataloged and listed online.

More details about the challenge, participation, and prizes can be found in Attribute Prediction

2. Data Challenge 2: Human Gait Recognition

Current surveillance solutions primarily rely on facial biometric identification using RGB videos or images acquired from CCTV cameras. These systems are significantly compromised when faces are fully or partially covered by masks, hijabs, turbans, or beards, and in cases of visual ambiguity due to changes in illumination, smog, dust, or fog. Additionally, these systems are highly dependent on the point-of-view and do not perform well over long distances. This challenge is prevalent in India and similar regions, especially when surveillance systems need to scale for large populations.

These vulnerabilities undermine the reliability of existing surveillance methods, necessitating the development of a more robust solution. Analyzing a person's identity through body shape and walking posture offers an excellent alternative. This method relies on the fact that each individual has a unique physiological structure, including height, head shape, leg bones, hip extension, muscles, and other factors.

To that end, gait recognition is less affected by obstructions and external visual factors, making it a more robust biometric for surveillance in challenging environments. Participants will be developing models for human gait recognition, using the classification of silhouettes obtained from RGB videos and point cloud data from LiDAR sensors.

More details about the challenge, participation details, and prizes can be found in Human Gait Recognition