Search Results for author: Yufan Chen

Found 22 papers, 17 papers with code

Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization

1 code implementation24 Dec 2024 Kunyu Peng, Di Wen, Sarfraz M. Saquib, Yufan Chen, Junwei Zheng, David Schneider, Kailun Yang, Jiamin Wu, Alina Roitberg, Rainer Stiefelhagen

Open-Set Domain Generalization (OSDG) is a challenging task requiring models to accurately predict familiar categories while minimizing confidence for unknown categories to effectively reject them in unseen domains.

Denoising Domain Generalization +3

Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler

1 code implementation26 Sep 2024 Kunyu Peng, Di Wen, Kailun Yang, Ao Luo, Yufan Chen, Jia Fu, M. Saquib Sarfraz, Alina Roitberg, Rainer Stiefelhagen

In this paper, we observe that an adaptive domain scheduler benefits more in OSDG compared with prefixed sequential and random domain schedulers.

Data Augmentation Domain Generalization +1

Periodic Trading Activities in Financial Markets: Mean-field Liquidation Game with Major-Minor Players

no code implementations18 Aug 2024 Yufan Chen, Lan Wu, Renyuan Xu, Ruixun Zhang

Motivated by recent empirical findings on the periodic phenomenon of aggregated market volumes in equity markets, we aim to understand the causes and consequences of periodic trading activities through a game-theoretic perspective, examining market interactions among different types of participants.

SMiCRM: A Benchmark Dataset of Mechanistic Molecular Images

no code implementations25 Jul 2024 Ching Ting Leung, Yufan Chen, Hanyu Gao

Comprising 453 images, it spans a broad array of organic chemical reactions, each illustrated with molecular structures and mechanistic arrows.

Benchmarking

Open Panoramic Segmentation

1 code implementation2 Jul 2024 Junwei Zheng, Ruiping Liu, Yufan Chen, Kunyu Peng, Chengzhi Wu, Kailun Yang, Jiaming Zhang, Rainer Stiefelhagen

To tackle this problem, in this work, we define a new task termed Open Panoramic Segmentation (OPS), where models are trained with FoV-restricted pinhole images in the source domain in an open-vocabulary setting while evaluated with FoV-open panoramic images in the target domain, enabling the zero-shot open panoramic semantic segmentation ability of models.

Open-Vocabulary Panoramic Semantic Segmentation

Referring Atomic Video Action Recognition

1 code implementation2 Jul 2024 Kunyu Peng, Jia Fu, Kailun Yang, Di Wen, Yufan Chen, Ruiping Liu, Junwei Zheng, Jiaming Zhang, M. Saquib Sarfraz, Rainer Stiefelhagen, Alina Roitberg

Since these existing methods underperform on RAVAR, we introduce RefAtomNet -- a novel cross-stream attention-driven method specialized for the unique challenges of RAVAR: the need to interpret a textual referring expression for the targeted individual, utilize this reference to guide the spatial localization and harvest the prediction of the atomic actions for the referring person.

Action Recognition Question Answering +4

FedPFT: Federated Proxy Fine-Tuning of Foundation Models

1 code implementation17 Apr 2024 Zhaopeng Peng, Xiaoliang Fan, Yufan Chen, Zheng Wang, Shirui Pan, Chenglu Wen, Ruisheng Zhang, Cheng Wang

Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs.

Federated Learning

RoDLA: Benchmarking the Robustness of Document Layout Analysis Models

1 code implementation CVPR 2024 Yufan Chen, Jiaming Zhang, Kunyu Peng, Junwei Zheng, Ruiping Liu, Philip Torr, Rainer Stiefelhagen

To address this, we are the first to introduce a robustness benchmark for DLA models, which includes 450K document images of three datasets.

Benchmarking Document Layout Analysis

Skeleton-Based Human Action Recognition with Noisy Labels

1 code implementation15 Mar 2024 Yi Xu, Kunyu Peng, Di Wen, Ruiping Liu, Junwei Zheng, Yufan Chen, Jiaming Zhang, Alina Roitberg, Kailun Yang, Rainer Stiefelhagen

In this study, we bridge this gap by implementing a framework that augments well-established skeleton-based human action recognition methods with label-denoising strategies from various research areas to serve as the initial benchmark.

Action Recognition Denoising +3

MolNexTR: A Generalized Deep Learning Model for Molecular Image Recognition

1 code implementation6 Mar 2024 Yufan Chen, Ching Ting Leung, Yong Huang, Jianwei Sun, Hao Chen, Hanyu Gao

In the field of chemical structure recognition, the task of converting molecular images into machine-readable data formats such as SMILES string stands as a significant challenge, primarily due to the varied drawing styles and conventions prevalent in chemical literature.

Data Augmentation Deep Learning

Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation

1 code implementation30 Jan 2024 Ruiping Liu, Jiaming Zhang, Kunyu Peng, Yufan Chen, Ke Cao, Junwei Zheng, M. Saquib Sarfraz, Kailun Yang, Rainer Stiefelhagen

Integrating information from multiple modalities enhances the robustness of scene perception systems in autonomous vehicles, providing a more comprehensive and reliable sensory framework.

Autonomous Vehicles Scene Segmentation

EPA: Neural Collapse Inspired Robust Out-of-Distribution Detector

no code implementations3 Jan 2024 Jiawei Zhang, Yufan Chen, Cheng Jin, Lei Zhu, Yuantao Gu

Out-of-distribution (OOD) detection plays a crucial role in ensuring the security of neural networks.

Out of Distribution (OOD) Detection

MonoGaussianAvatar: Monocular Gaussian Point-based Head Avatar

no code implementations7 Dec 2023 Yufan Chen, Lizhen Wang, Qijing Li, Hongjiang Xiao, Shengping Zhang, Hongxun Yao, Yebin Liu

In response to these challenges, we propose MonoGaussianAvatar (Monocular Gaussian Point-based Head Avatar), a novel approach that harnesses 3D Gaussian point representation coupled with a Gaussian deformation field to learn explicit head avatars from monocular portrait videos.

Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision

1 code implementation21 Sep 2023 Yiping Wei, Kunyu Peng, Alina Roitberg, Jiaming Zhang, Junwei Zheng, Ruiping Liu, Yufan Chen, Kailun Yang, Rainer Stiefelhagen

These works overlooked the differences in performance among modalities, which led to the propagation of erroneous knowledge between modalities while only three fundamental modalities, i. e., joints, bones, and motions are used, hence no additional modalities are explored.

Action Recognition Knowledge Distillation +3

Open Scene Understanding: Grounded Situation Recognition Meets Segment Anything for Helping People with Visual Impairments

1 code implementation15 Jul 2023 Ruiping Liu, Jiaming Zhang, Kunyu Peng, Junwei Zheng, Ke Cao, Yufan Chen, Kailun Yang, Rainer Stiefelhagen

Grounded Situation Recognition (GSR) is capable of recognizing and interpreting visual scenes in a contextually intuitive way, yielding salient activities (verbs) and the involved entities (roles) depicted in images.

Decoder Grounded Situation Recognition +2

Few Shot Medical Image Segmentation with Cross Attention Transformer

1 code implementation24 Mar 2023 Yi Lin, Yufan Chen, Kwang-Ting Cheng, Hao Chen

Our proposed network mines the correlations between the support image and query image, limiting them to focus only on useful foreground information and boosting the representation capacity of both the support prototype and query features.

Few-Shot Learning Image Segmentation +4

Neural Inertial Localization

1 code implementation CVPR 2022 Sachini Herath, David Caruso, Chen Liu, Yufan Chen, Yasutaka Furukawa

This paper proposes the inertial localization problem, the task of estimating the absolute location from a sequence of inertial sensor measurements.

Indoor Localization Privacy Preserving

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