79 citations · 608 across the 98 of their papers we have counts for
21 papers · 2 filters
Exploring Event-driven Dynamic Context for Accident Scene Segmentation
Jiaming Zhang, Kailun Yang, Rainer Stiefelhagen
The robustness of semantic segmentation on edge cases of traffic scene is a vital factor for the safety of intelligent transportation. However, most of the critical scenes of traff…
Affect-DML: Context-Aware One-Shot Recognition of Human Affect using Deep Metric Learning
Kunyu Peng, Alina Roitberg, David Schneider +3
Human affect recognition is a well-established research area with numerous applications, e.g., in psychological care, but existing methods assume that all emotions-of-interest are…
Certainty Volume Prediction for Unsupervised Domain Adaptation
Tobias Ringwald, Rainer Stiefelhagen
Unsupervised domain adaptation (UDA) deals with the problem of classifying unlabeled target domain data while labeled data is only available for a different source domain. Unfortun…
UBRS: Uncertainty-Based Resampling and Reweighting Strategy for Unsupervised Domain Adaptation
Tobias Ringwald, Rainer Stiefelhagen
Unsupervised domain adaptation (UDA) deals with the adaptation process of a model to an unlabeled target domain while annotated data is only available for a given source domain. Th…
Transfer beyond the Field of View: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation
Jiaming Zhang, Chaoxiang Ma, Kailun Yang +3
Autonomous vehicles clearly benefit from the expanded Field of View (FoV) of 360-degree sensors, but modern semantic segmentation approaches rely heavily on annotated training data…
Trans4Trans: Efficient Transformer for Transparent Object and Semantic Scene Segmentation in Real-World Navigation Assistance
Jiaming Zhang, Kailun Yang, Angela Constantinescu +3
Transparent objects, such as glass walls and doors, constitute architectural obstacles hindering the mobility of people with low vision or blindness. For instance, the open space b…
Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World
Jiaming Zhang, Kailun Yang, Angela Constantinescu +3
Common fully glazed facades and transparent objects present architectural barriers and impede the mobility of people with low vision or blindness, for instance, a path detected beh…
Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation
Haobin Tan, Chang Chen, Xinyu Luo +4
Lacking the ability to sense ambient environments effectively, blind and visually impaired people (BVIP) face difficulty in walking outdoors, especially in urban areas. Therefore,…
DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange
Chaoxiang Ma, Jiaming Zhang, Kailun Yang +2
Intelligent vehicles clearly benefit from the expanded Field of View (FoV) of the 360-degree sensors, but the vast majority of available semantic segmentation training images are c…
Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games
Alina Roitberg, David Schneider, Aulia Djamal +3
Recognizing Activities of Daily Living (ADL) is a vital process for intelligent assistive robots, but collecting large annotated datasets requires time-consuming temporal labeling…
HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor
Huayao Liu, Ruiping Liu, Kailun Yang +3
Independently exploring unknown spaces or finding objects in an indoor environment is a daily but challenging task for visually impaired people. However, common 2D assistive system…
MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding
Kunyu Peng, Juncong Fei, Kailun Yang +6
At the heart of all automated driving systems is the ability to sense the surroundings, e.g., through semantic segmentation of LiDAR sequences, which experienced a remarkable progr…
Pose2Drone: A Skeleton-Pose-based Framework for Human-Drone Interaction
Zdravko Marinov, Stanka Vasileva, Qing Wang +3
Drones have become a common tool, which is utilized in many tasks such as aerial photography, surveillance, and delivery. However, operating a drone requires more and more interact…
Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentation
Simon Reiß, Constantin Seibold, Alexander Freytag +2
Pixel-wise segmentation is one of the most data and annotation hungry tasks in our field. Providing representative and accurate annotations is often mission-critical especially for…
Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation
M. Saquib Sarfraz, Naila Murray, Vivek Sharma +3
Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks. For this…
Capturing Omni-Range Context for Omnidirectional Segmentation
Kailun Yang, Jiaming Zhang, Simon Reiß +2
Convolutional Networks (ConvNets) excel at semantic segmentation and have become a vital component for perception in autonomous driving. Enabling an all-encompassing view of street…
Perception Framework through Real-Time Semantic Segmentation and Scene Recognition on a Wearable System for the Visually Impaired
Yingzhi Zhang, Haoye Chen, Kailun Yang +2
As the scene information, including objectness and scene type, are important for people with visual impairment, in this work we present a multi-task efficient perception system for…
Panoptic Lintention Network: Towards Efficient Navigational Perception for the Visually Impaired
Wei Mao, Jiaming Zhang, Kailun Yang +1
Classic computer vision algorithms, instance segmentation, and semantic segmentation can not provide a holistic understanding of the surroundings for the visually impaired. In this…
DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection
Shuo Chen, Kailun Yang, Rainer Stiefelhagen
Street scene change detection continues to capture researchers' interests in the computer vision community. It aims to identify the changed regions of the paired street-view images…
Panoramic Panoptic Segmentation: Towards Complete Surrounding Understanding via Unsupervised Contrastive Learning
Alexander Jaus, Kailun Yang, Rainer Stiefelhagen
In this work, we introduce panoramic panoptic segmentation as the most holistic scene understanding both in terms of field of view and image level understanding for standard camera…
Uncertainty-sensitive Activity Recognition: a Reliability Benchmark and the CARING Models
Alina Roitberg, Monica Haurilet, Manuel Martinez +1
Beyond assigning the correct class, an activity recognition model should also be able to determine, how certain it is in its predictions. We present the first study of how welthe c…