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Peng Yuan

14 accepted papers

2025

Dynamical phases of short-term memory mechanisms in RNNs

ICML 2025poster

Short-term memory is essential for cognitive processing, yet our understanding of its neural mechanisms remains unclear. Neuroscience has long focused on how sequential activity patterns, where neurons fire one after another within large networks, can explain how information is maintained. While rec…

Cited by 0SourcePDFScholar
2025

Incident Diagnosing and Reporting System Based on Retrieval Augmented Large Language Model

AAAI 2025technical

The Internet of Things (IoT) is widely used in many applications such as smart city, transportation, healthcare, and environment monitoring. A key task of IoT maintenance is to analyze the abnormal sensor records and generate incident report. Traditionally, domain experts engage in such labor intens…

Cited by 0SourcePDFScholar
2023

AttentionShift: Iteratively Estimated Part-Based Attention Map for Pointly Supervised Instance Segmentation

CVPR 2023poster

Pointly supervised instance segmentation (PSIS) learns to segment objects using a single point within the object extent as supervision. Challenged by the non-negligible semantic variance between object parts, however, the single supervision point causes semantic bias and false segmentation. In this…

Cited by 12SourcePDFScholar
2022

CF-DETR: Coarse-to-Fine Transformers for End-to-End Object Detection

AAAI 2022technical

The recently proposed DEtection TRansformer (DETR) achieves promising performance for end-to-end object detection. However, it has relatively lower detection performance on small objects and suffers from slow convergence. This paper observed that DETR performs surprisingly well even on small objects…

Cited by 41SourcePDFScholar
2022

End-to-End Weakly Supervised Object Detection with Sparse Proposal Evolution

ECCV 2022poster

"Conventional methods for weakly supervised object detection (WSOD) typically enumerate dense proposals and select the discriminative proposals as objects. However, these two-stage “enumerate-and-select” methods suffer object feature ambiguity brought by dense proposals and low detection efficiency…

2022

PANDORA: A Panoramic Detection Dataset for Object with Orientation

ECCV 2022poster

"Panoramic images have become increasingly popular as omnidirectional panoramic technology has advanced. Many datasets and works resort to object detection to better understand the content of the panoramic image. These datasets and detectors use a Bounding Field of View (BFoV) as a bounding box in p…

2022

PRINCE: Prefix-Masked Decoding for Knowledge Enhanced Sequence-to-Sequence Pre-Training

EMNLP 2022main

Pre-trained Language Models (PLMs) have shown effectiveness in various Natural Language Processing (NLP) tasks. Denoising autoencoder is one of the most successful pre-training frameworks, learning to recompose the original text given a noise-corrupted one. The existing studies mainly focus on injec…

2022

Semi-Supervised Object Detection via Multi-Instance Alignment With Global Class Prototypes

CVPR 2022poster

Semi-Supervised object detection (SSOD) aims to improve the generalization ability of object detectors with large-scale unlabeled images. Current pseudo-labeling-based SSOD methods individually learn from labeled data and unlabeled data, without considering the relation between them. To make full us…

Cited by 21PDFScholar
2022

Simulation-and-Mining: Towards Accurate Source-Free Unsupervised Domain Adaptive Object Detection

ICASSP 2022accepted

Vanilla unsupervised domain adaptive (UDA) object detection typically requires the labeled source data for joint-training with the unlabeled target data, which is usually unavailable in real-world scenarios due to data privacy, leading to source data-free UDA object detection. Herein, we first analy…

Cited by 0SourceScholar
2022

Unbiased IoU for Spherical Image Object Detection

AAAI 2022technical

As one of the fundamental components of object detection, intersection-over-union (IoU) calculations between two bounding boxes play an important role in samples selection, NMS operation and evaluation of object detection algorithms. This procedure is well-defined and solved for planar images, while…

Cited by 12SourcePDFScholar
2021

A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data

AAAI 2021technical

Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the data privacy and the inefficiency of data transmission, it is impractical in real scenarios. Hence, it draws our eyes t…

Cited by 168SourcePDFScholar
2021

K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce

EMNLP 2021finding

Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domai…

2021

Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization

EMNLP 2021main

The copying mechanism has had considerable success in abstractive summarization, facilitating models to directly copy words from the input text to the output summary. Existing works mostly employ encoder-decoder attention, which applies copying at each time step independently of the former ones. How…

2020

On the Faithfulness for E-commerce Product Summarization

COLING 2020main

In this work, we present a model to generate e-commerce product summaries. The consistency between the generated summary and the product attributes is an essential criterion for the ecommerce product summarization task. To enhance the consistency, first, we encode the product attribute table to guid…