# ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes

arXiv: https://arxiv.org/abs/2106.02740 · HTML: https://mlsanity.com/paper/2106.02740/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 62 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [Semi-Supervised Semantic Segmentation with Cross-Consistency Training](https://mlsanity.com/paper/2003.09005/) (2020)
- [Panoptic-DeepLab](https://mlsanity.com/paper/1910.04751/) (2019)
- [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018)
- [Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1803.10464/) (2018)
- [Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2004.04581/) (2020)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation](https://mlsanity.com/paper/1603.06098/) (2016)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [Scale-Aware Trident Networks for Object Detection](https://mlsanity.com/paper/1901.01892/) (2019)
- [Semi-Supervised Semantic Segmentation with High- and Low-level Consistency](https://mlsanity.com/paper/1908.05724/) (2019)
- [Weakly-Supervised Semantic Segmentation via Sub-category Exploration](https://mlsanity.com/paper/2008.01183/) (2020)
- [Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2105.08965/) (2021)
- [Constrained Convolutional Neural Networks for Weakly Supervised Segmentation](https://mlsanity.com/paper/1506.03648/) (2015)
- [Bootstrapping Semantic Segmentation with Regional Contrast](https://mlsanity.com/paper/2104.04465/) (2021)

## Built on by (most cited first, 30 of 47)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2303.11797/) (2023)
- [DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks](https://mlsanity.com/paper/2502.17157/) (2025)
- [What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2306.15521/) (2023)
- [Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts](https://mlsanity.com/paper/2303.14152/) (2023)
- [ZIM: Zero-Shot Image Matting for Anything](https://mlsanity.com/paper/2411.00626/) (2024)
- [TRansPose: Large-Scale Multispectral Dataset for Transparent Object](https://mlsanity.com/paper/2307.05016/) (2023)
- [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2503.12781/) (2025)
- [ASAM: Boosting Segment Anything Model with Adversarial Tuning](https://mlsanity.com/paper/2405.00256/) (2024)
- [TranSplat: Surface Embedding-guided 3D Gaussian Splatting for Transparent Object Manipulation](https://mlsanity.com/paper/2502.07840/) (2025)
- [SpectralWaste Dataset: Multimodal Data for Waste Sorting Automation](https://mlsanity.com/paper/2403.18033/) (2024)
- [Vision as Unified Multimodal Generation](https://mlsanity.com/paper/2607.06560/) (2026)
- [VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting](https://mlsanity.com/paper/2303.14828/) (2023)
- [Stable Segment Anything Model](https://mlsanity.com/paper/2311.15776/) (2023)
- [High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2412.11464/) (2024)
- [Segment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation](https://mlsanity.com/paper/2506.11131/) (2025)
- [Show or Tell? Effectively prompting Vision-Language Models for semantic segmentation](https://mlsanity.com/paper/2503.19647/) (2025)
- [The State of Computer Vision Research in Africa](https://mlsanity.com/paper/2401.11617/) (2024)
- [Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation](https://mlsanity.com/paper/2501.04696/) (2025)
- [FANet: Feature Amplification Network for Semantic Segmentation in Cluttered Background](https://mlsanity.com/paper/2407.09379/) (2024)
- [GlobalWasteData: A Large-Scale, Integrated Dataset for Robust Waste Classification and Environmental Monitoring](https://mlsanity.com/paper/2602.07463/) (2026)
- [WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks](https://mlsanity.com/paper/2409.16999/) (2024)
- [COSNet: A Novel Semantic Segmentation Network using Enhanced Boundaries in Cluttered Scenes](https://mlsanity.com/paper/2410.24139/) (2024)
- [Temporal-consistent CAMs for Weakly Supervised Video Segmentation in Waste Sorting](https://mlsanity.com/paper/2502.01455/) (2025)
- [Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation](https://mlsanity.com/paper/2505.06280/) (2025)
- [Learning Using Privileged Information for Litter Detection](https://mlsanity.com/paper/2508.04124/) (2025)
- [StreetView-Waste: A Multi-Task Dataset for Urban Waste Management](https://mlsanity.com/paper/2511.16440/) (2025)
- [An Ensemble Learning Approach towards Waste Segmentation in Cluttered Environment](https://mlsanity.com/paper/2602.13681/) (2026)
- [Visual Material Characteristics Learning for Circular Healthcare](https://mlsanity.com/paper/2309.04763/) (2023)

## Abstract (arXiv)

Less than 35% of recyclable waste is being actually recycled in the US, which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. We believe that ZeroWaste will catalyze research in object detection and semantic segmentation in extreme clutter as well as applications in the recycling domain. Our project page can be found at http://ai.bu.edu/zerowaste/.