Author name cluster

Han Deng

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3 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

CPRet: A Dataset, Benchmark, and Model for Retrieval in Competitive Programming

Han Deng
Yuan Meng
Shixiang Tang
Wanli Ouyang
Xinzhu Ma

Competitive programming is widely used to evaluate the coding and reasoning abilities of large language models. However, the growing presence of duplicate or highly similar problems raises concerns not only about competition fairness, but also about the validity of competitive programming as a benchmark for model evaluation. We introduce a retrieval-oriented benchmark suite for competitive programming, covering four retrieval tasks—two code-centric (Text-to-Code, Code-to-Code) and two newly proposed problem-centric tasks (Problem-to-Duplicate, Simplified-to-Full)—built from a combination of automatically crawled problem–solution data and manually curated annotations. Our contribution includes both high-quality training data and temporally separated test sets for reliable evaluation. We develop two task-specialized retrievers based on this dataset: CPRetriever-Code, trained with a novel Group-InfoNCE loss for problem–code alignment, and CPRetriever-Prob, fine-tuned for problem-level similarity. Both models achieve strong results and are open-sourced for local use. Finally, we analyze LiveCodeBench and find that high-similarity problems inflate model pass rates and reduce differentiation, underscoring the need for similarity-aware evaluation in future benchmarks.

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ICRA Conference 2024 Conference Paper

STT: Stateful Tracking with Transformers for Autonomous Driving

Longlong Jing
Ruichi Yu
Xu Chen
Zhengli Zhao
Shiwei Sheng
Colin Graber
Qi Chen
Qinru Li

Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and accurately estimate their states such as velocity and acceleration in the present. Existing works frequently focus on the association task while either neglecting the model’s performance on state estimation or deploying complex heuristics to predict the states. In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately. STT consumes rich appearance, geometry, and motion signals through long term history of detections and is jointly optimized for both data association and state estimation tasks. Since the standard tracking metrics like MOTA and MOTP do not capture the combined performance of the two tasks in the wider spectrum of object states, we extend them with new metrics called S-MOTA and MOTP S that address this limitation. STT achieves competitive real-time performance on the Waymo Open Dataset.

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ICRA Conference 2022 Conference Paper

Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking

Longlong Jing
Ruichi Yu
Henrik Kretzschmar
Kang Li
Charles R. Qi
Hang Zhao 0021
Alper Ayvaci
Xu Chen

Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving. Approaches to monocular 3D perception including detection and tracking, however, often yield inferior performance when compared to LiDAR-based techniques. Through systematic analysis, we identified that per-object depth estimation accuracy is a major factor bounding the performance. Motivated by this observation, we propose a multi-level fusion method that combines different representations (RGB and pseudo-LiDAR) and temporal information across multiple frames for objects (tracklets) to enhance per-object depth estimation. Our proposed fusion method achieves the state-of-the-art performance of per-object depth estimation on the Waymo Open Dataset, the KITTI detection dataset, and the KITTI MOT dataset. We further demonstrate that by simply replacing estimated depth with fusion-enhanced depth, we can achieve significant improvements in monocular 3D perception tasks, including detection and tracking.

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