Sustainability

Blog Posts

Using Machine Learning to Increase Durability and Reduce Returns for Sports and Fashion Goods

Ali Naeem and Alan Fortuny Sicart, January 27, 2024
Circularity in Fashion, powered by AI

Alan Fortuny Sicart, February 13, 2023
The Green Last Mile Project: Accelerating Cargo-Bike Logistics in Cities

Nicolas Collignon, October 11, 2022

Webinars

Decarbonizing AI: The Good, the Bad, and the Ugly

Dr. Noman Bashir (University of Massachusetts Amherst), July 28, 2023

Workshop Papers

Venue	Title
NeurIPS 2023	How to Recycle: General Vision-Language Model without Task Tuning for Predicting Object Recyclability (Papers Track) Abstract and authors: (click to expand) Abstract: Waste segregation and recycling place a crucial role in fostering environmental sustainability. However, discerning the whether a material is recyclable or not poses a formidable challenge, primarily because of inadequate recycling guidelines to accommodate a diverse spectrum of objects and their varying conditions. We investigated the role of vision-language models in addressing this challenge. We curated a dataset consisting >1000 images across 11 disposal categories for optimal discarding and assessed the applicability of general vision-language models for recyclability classification. Our results show that Contrastive Language-Image Pre- training (CLIP) model, which is pretrained to understand the relationship between images and text, demonstrated remarkable performance in the zero-shot recycla- bility classification task, with an accuracy of 89%. Our results underscore the potential of general vision-language models in addressing real-world challenges, such as automated waste sorting, by harnessing the inherent associations between visual and textual information. Authors: Eliot Park (Harvard Medical School); Eddy Pan (Harvard Medical School); Shreya Johri (Harvard Medical School); Pranav Rajpurkar (Harvard Medical School)

Venue

Title

NeurIPS 2023

How to Recycle: General Vision-Language Model without Task Tuning for Predicting Object Recyclability (Papers Track)

Abstract and authors: (click to expand)

Abstract: Waste segregation and recycling place a crucial role in fostering environmental sustainability. However, discerning the whether a material is recyclable or not poses a formidable challenge, primarily because of inadequate recycling guidelines to accommodate a diverse spectrum of objects and their varying conditions. We investigated the role of vision-language models in addressing this challenge. We curated a dataset consisting >1000 images across 11 disposal categories for optimal discarding and assessed the applicability of general vision-language models for recyclability classification. Our results show that Contrastive Language-Image Pre- training (CLIP) model, which is pretrained to understand the relationship between images and text, demonstrated remarkable performance in the zero-shot recycla- bility classification task, with an accuracy of 89%. Our results underscore the potential of general vision-language models in addressing real-world challenges, such as automated waste sorting, by harnessing the inherent associations between visual and textual information.

Authors: Eliot Park (Harvard Medical School); Eddy Pan (Harvard Medical School); Shreya Johri (Harvard Medical School); Pranav Rajpurkar (Harvard Medical School)