Author name cluster

Xiaopeng Wei

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

AAAI Conference 2025 Conference Paper

Separating the Wheat from the Chaff: Spatio-Temporal Transformer with View-interweaved Attention for Photon-Efficient Depth Sensing

Letian Yu
Jiaxi Yang
Bo Dong
Qirui Bao
Yuanbo Wang
Felix Heide
Xiaopeng Wei
Xin Yang

Time-resolved imaging is an emerging sensing modality that has been shown to enable advanced applications, including remote sensing, fluorescence lifetime imaging, and even non-line-of-sight sensing. Single-photon avalanche diodes (SPADs) outperform relevant time-resolved imaging technologies thanks to their excellent photon sensitivity and superior temporal resolution on the order of tens of picoseconds. The capability of exceeding the sensing limits of conventional cameras for SPADs also draws attention to the photon-efficient imaging area. However, photon-efficient imaging under degraded conditions with low photon counts and low signal-to-background ratio (SBR) still remains an inevitable challenge. In this paper, we propose a spatio-temporal transformer network for photon-efficient imaging under low-flux scenarios. In particular, we introduce a view-interweaved attention mechanism (VIAM) to extract both spatial-view and temporal-view self-attention in each transformer block. We also design an adaptive-weighting scheme to dynamically adjust the weights between different views of self-attention in VIAM for different signal-to-background levels. We extensively validate and demonstrate the effectiveness of our approach on the simulated Middlebury dataset and a specially self-collected dataset with real-world-captured SPAD measurements and well-annotated ground truth depth maps.

PDF Details DOI

IROS Conference 2024 Conference Paper

Event-intensity Stereo with Cross-modal Fusion and Contrast

Yuanbo Wang
Shanglai Qu
Tianyu Meng
Yan Cui
Haiyin Piao
Xiaopeng Wei
Xin Yang 0011

For binocular stereo, traditional cameras excel in capturing fine details and texture information but are limited in terms of dynamic range and their ability to handle rapid motion. On the contrary, event cameras provide pixel-level intensity changes with low latency and a wide dynamic range, albeit at the cost of less detail in their output. It is natural to leverage the strengths of both modalities. We solve this problem by introducing a cross-modal fusion module that learns a visual representation from both sensor inputs. Additionally, we extract and compare dense event-intensity stereo pair features by contrasting “pairs of event-intensity pairs from different views and different modalities and different timestamps”. This provides the flexibility in masking hard negatives and enables networks to effectively combine event-intensity signals within a contrastive learning framework, leading to an improved matching accuracy and facilitating more accurate estimation of disparity. Experimental results validate the effectiveness of our model and the improvement of disparity estimation accuracy.

Details

JBHI Journal 2024 Journal Article

Eye Gaze Guided Cross-Modal Alignment Network for Radiology Report Generation

Peixi Peng
Wanshu Fan
Yue Shen
Wenfei Liu
Xin Yang
Qiang Zhang
Xiaopeng Wei
Dongsheng Zhou

The potential benefits of automatic radiology report generation, such as reducing misdiagnosis rates and enhancing clinical diagnosis efficiency, are significant. However, existing data-driven methods lack essential medical prior knowledge, which hampers their performance. Moreover, establishing global correspondences between radiology images and related reports, while achieving local alignments between images correlated with prior knowledge and text, remains a challenging task. To address these shortcomings, we introduce a novel Eye Gaze Guided Cross-modal Alignment Network (EGGCA-Net) for generating accurate medical reports. Our approach incorporates prior knowledge from radiologists' Eye Gaze Region (EGR) to refine the fidelity and comprehensibility of report generation. Specifically, we design a Dual Fine-Grained Branch (DFGB) and a Multi-Task Branch (MTB) to collaboratively ensure the alignment of visual and textual semantics across multiple levels. To establish fine-grained alignment between EGR-related images and sentences, we introduce the Sentence Fine-grained Prototype Module (SFPM) within DFGB to capture cross-modal information at different levels. Additionally, to learn the alignment of EGR-related image topics, we introduce the Multi-task Feature Fusion Module (MFFM) within MTB to refine the encoder output information. Finally, a specifically designed label matching mechanism is designed to generate reports that are consistent with the anticipated disease states. The experimental outcomes indicate that the introduced methodology surpasses previous advanced techniques, yielding enhanced performance on two extensively used benchmark datasets: Open-i and MIMIC-CXR.

Details DOI

IJCAI Conference 2018 Conference Paper

Active Object Reconstruction Using a Guided View Planner

Xin Yang
Yuanbo Wang
Yaru Wang
Baocai Yin
Qiang Zhang
Xiaopeng Wei
Hongbo Fu

Inspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner. We aim to reconstruct a 3D model using images observed from a planned sequence of informative and discriminative views. But where are such informative and discriminative views around an object? To address this we propose a unified model for view planning and object reconstruction, which is utilized to learn a guided information acquisition model and to aggregate information from a sequence of images for reconstruction. Experiments show that our model (1) increases our reconstruction accuracy with an increasing number of views (2) and generally predicts a more informative sequence of views for object reconstruction compared to other alternative methods.

PDF Details

AAAI Conference 2017 Conference Paper

Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug-Drug Interaction

Bo Jin
Haoyu Yang
Cao Xiao
Ping Zhang
Xiaopeng Wei
Fei Wang

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality around the world. Identifying potential DDIs during the drug design process is critical in guiding targeted clinical drug safety testing. Although detection of adverse DDIs is conducted during Phase IV clinical trials, there are still a large number of new DDIs founded by accidents after the drugs were put on market. With the arrival of big data era, more and more pharmaceutical research and development data are becoming available, which provides an invaluable resource for digging insights that can potentially be leveraged in early prediction of DDIs. Many computational approaches have been proposed in recent years for DDI prediction. However, most of them focused on binary prediction (with or without DDI), despite the fact that each DDI is associated with a different type. Predicting the actual DDI type will help us better understand the DDI mechanism and identify proper ways to prevent it. In this paper, we formulate the DDI type prediction problem as a multitask dyadic regression problem, where the prediction of each speciﬁc DDI type is treated as a task. Compared with conventional matrix completion approaches which can only impute the missing entries in the DDI matrix, our approach can directly regress those dyadic relationships (DDIs) and thus can be extend to new drugs more easily. We developed an effective proximal gradient method to solve the problem. Evaluation on real world datasets is presented to demonstrate the effectiveness of the proposed approach.

PDF Details