activity
20152023
most citedDeep Perceptual Mapping for Cross-Modal Face Recognition

79 citations · 608 across the 98 of their papers we have counts for

collaborators

105 papers

cs.CV2023★ 1 cited

CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity

Hao Shi, Chengshan Pang, Jiaming Zhang +6

Roadside camera-driven 3D object detection is a crucial task in intelligent transportation systems, which extends the perception range beyond the limitations of vision-centric vehi…

cs.CV2023

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

Yiping Wei, Kunyu Peng, Alina Roitberg +6

Self-supervised representation learning for human action recognition has developed rapidly in recent years. Most of the existing works are based on skeleton data while using a mult…

cs.CV2023★ 1 cited

Towards Privacy-Supporting Fall Detection via Deep Unsupervised RGB2Depth Adaptation

Hejun Xiao, Kunyu Peng, Xiangsheng Huang +4

Fall detection is a vital task in health monitoring, as it allows the system to trigger an alert and therefore enabling faster interventions when a person experiences a fall. Altho…

cs.CV2023★ 1 cited

On Transferability of Driver Observation Models from Simulated to Real Environments in Autonomous Cars

Walter Morales-Alvarez, Novel Certad, Alina Roitberg +2

For driver observation frameworks, clean datasets collected in controlled simulated environments often serve as the initial training ground. Yet, when deployed under real driving c…

eess.IV2023★ 3 cited

Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines

Alexander Jaus, Constantin Seibold, Kelsey Hermann +5

In this study, we present a method for generating automated anatomy segmentation datasets using a sequential process that involves nnU-Net-based pseudo-labeling and anatomy-guided…

cs.CV2023★ 1 cited

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

Ruiping Liu, Jiaming Zhang, Kunyu Peng +5

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

cs.CV2023

Line Graphics Digitization: A Step Towards Full Automation

Omar Moured, Jiaming Zhang, Alina Roitberg +2

The digitization of documents allows for wider accessibility and reproducibility. While automatic digitization of document layout and text content has been a long-standing focus of…

cs.CV2023★ 1 cited

OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation

Fei Teng, Jiaming Zhang, Kunyu Peng +3

Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly…

cs.RO2023★ 1 cited

Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents

Ke Cao, Ruiping Liu, Ze Wang +6

The mobile robot relies on SLAM (Simultaneous Localization and Mapping) to provide autonomous navigation and task execution in complex and unknown environments. However, it is hard…

eess.IV2023★ 2 cited

Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling

Constantin Seibold, Alexander Jaus, Matthias A. Fink +5

Purpose: Interpreting chest radiographs (CXR) remains challenging due to the ambiguity of overlapping structures such as the lungs, heart, and bones. To address this issue, we prop…

cs.CV2023★ 1 cited

READMem: Robust Embedding Association for a Diverse Memory in Unconstrained Video Object Segmentation

Stéphane Vujasinović, Sebastian Bullinger, Stefan Becker +3

We present READMem (Robust Embedding Association for a Diverse Memory), a modular framework for semi-automatic video object segmentation (sVOS) methods designed to handle unconstra…

cs.CV2023★ 1 cited

Exploring Few-Shot Adaptation for Activity Recognition on Diverse Domains

Kunyu Peng, Di Wen, David Schneider +5

Domain adaptation is essential for activity recognition to ensure accurate and robust performance across diverse environments, sensor types, and data sources. Unsupervised domain a…

cs.CV2023

FishDreamer: Towards Fisheye Semantic Completion via Unified Image Outpainting and Segmentation

Hao Shi, Yu Li, Kailun Yang +7

This paper raises the new task of Fisheye Semantic Completion (FSC), where dense texture, structure, and semantics of a fisheye image are inferred even beyond the sensor field-of-v…

eess.IV2023

Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging

Zdravko Marinov, Simon Reiß, David Kersting +2

Positron Emission Tomography (PET) and Computer Tomography (CT) are routinely used together to detect tumors. PET/CT segmentation models can automate tumor delineation, however, cu…

cs.CV2023★ 6 cited

Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images

Zdravko Marinov, Rainer Stiefelhagen, Jens Kleesiek

Interactive segmentation reduces the annotation time of medical images and allows annotators to iteratively refine labels with corrective interactions, such as clicks. While existi…

cs.CV2023★ 5 cited

Delivering Arbitrary-Modal Semantic Segmentation

Jiaming Zhang, Ruiping Liu, Hao Shi +6

Multimodal fusion can make semantic segmentation more robust. However, fusing an arbitrary number of modalities remains underexplored. To delve into this problem, we create the DeL…

cs.CV2023

360BEV: Panoramic Semantic Mapping for Indoor Bird's-Eye View

Zhifeng Teng, Jiaming Zhang, Kailun Yang +5

Seeing only a tiny part of the whole is not knowing the full circumstance. Bird's-eye-view (BEV) perception, a process of obtaining allocentric maps from egocentric views, is restr…

cs.RO2023

MateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments

Junwei Zheng, Jiaming Zhang, Kailun Yang +2

People with Visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-e…

cs.CV2023★ 10 cited

Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues

Verena Jasmin Hallitschke, Tobias Schlumberger, Philipp Kataliakos +6

Recently, deep learning enabled the accurate segmentation of various diseases in medical imaging. These performances, however, typically demand large amounts of manual voxel annota…

cs.CV2022★ 1 cited

Uncertainty-aware Vision-based Metric Cross-view Geolocalization

Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner +2

This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial imag…

cs.CV2022★ 1 cited

Anticipative Feature Fusion Transformer for Multi-Modal Action Anticipation

Zeyun Zhong, David Schneider, Michael Voit +2

Although human action anticipation is a task which is inherently multi-modal, state-of-the-art methods on well known action anticipation datasets leverage this data by applying ens…

eess.IV2022★ 7 cited

AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier

Lars Heiliger, Zdravko Marinov, Max Hasin +9

Tumor volume and changes in tumor characteristics over time are important biomarkers for cancer therapy. In this context, FDG-PET/CT scans are routinely used for staging and re-sta…

cs.CV2022★ 7 cited

Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding

Constantin Seibold, Simon Reiß, Saquib Sarfraz +7

In clinical radiology reports, doctors capture important information about the patient's health status. They convey their observations from raw medical imaging data about the inner…

cs.CV2022★ 3 cited

ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization

Zdravko Marinov, Alina Roitberg, David Schneider +1

Modality selection is an important step when designing multimodal systems, especially in the case of cross-domain activity recognition as certain modalities are more robust to doma…