Author name cluster

Yang Dong

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3 papers

1 author row

AAAI Conference 2025 Conference Paper

UrbanWaste: In-the-Bin Dataset for Waste Disposal Inspection with Multi-Granularity Hierarchical Labels

Zhuoqi Ma
Zejun You
Yang Dong
Yukai Liu
Xiyue Gao
Qiguang Miao

Our world faces the challenge of efficiently and responsibly managing the ever-growing volume of urban waste. Many countries and regions have implemented categorized trash bins and require residents to sort their waste according to specified criteria. Proper waste classification by residents significantly reduces the workload in the waste disposal process. However, due to the lack of effective supervision during classification, the quality of waste sorting is often compromised. This misclassification can lead to higher pollution risks, lower recycling rates, and increased waste management costs and difficulties. To address this issue, we propose using images captured from within trash bins to supervise garbage delivery. We introduce UrbanWaste, an image dataset specifically designed for in-the-bin waste detection and segmentation. The dataset includes 25,254 RGB images and 140,008 annotated items, featuring dense annotations and multi-granularity labels across 193 distinct waste categories. We evaluated state-of-the-art segmentation models to understand their generalization and performance on UrbanWaste. Based on this dataset, we developed a comprehensive workflow for waste classification inspection, which has been deployed in real-world districts to assess the system's effectiveness. We hope UrbanWaste will inspire new directions in AI research for environmental sustainability.

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AAMAS Conference 2024 Conference Paper

ANOTO: Improving Automated Negotiation via Offline-to-Online Reinforcement Learning

Siqi Chen
Jianing Zhao
Kai Zhao
Gerhard Weiss
Fengyun Zhang
Ran Su
Yang Dong
Daqian Li

Automated negotiation is a crucial component for establishing cooperation and collaboration within multi-agent systems. While reinforcement learning (RL)-based negotiating agents have achieved remarkable success in various scenarios, they still face limitations due to certain assumptions on which they are based. In this work, we proposes a novel approach called ANOTO to improve the negotiating agents’ ability via offline-to-online RL. ANOTO enables a negotiating agent (1) to communicate with opponents using an end-to-end strategy that covers all negotiation actions, (2) to learn negotiation strategies from historical offline data without requiring active interactions, and (3) to enhance the optimization process during the online phase, facilitating rapid and stable performance improvements for the learned offline strategies. Experimental results, based on a number of negotiation scenarios and recent winning agents from the Automated Negotiating Agents Competitions (ANAC), are provided.

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AAAI Conference 2023 Conference Paper

Learn from Yesterday: A Semi-supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams

Yongrui Chen
Xinnan Guo
Tongtong Wu
Guilin Qi
Yang Li
Yang Dong

Conventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data and high retraining costs. The former tends to cause overfitting on unseen databases for the new task, while the latter makes a full review of instances from past tasks impractical for the model, resulting in forgetting of learned SQL structures and database schemas. To address the problems, this paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-SQL tasks and offers two promising solutions in turn. The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks. The improved solution SFNet takes advantage of the intrinsic connection between CL and SSL. It uses in-memory past information to help current SSL, while adding high-quality pseudo instances in memory to improve future replay. The experiments on two datasets shows that SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple metrics.

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