27 citations · 80 across the 96 of their papers we have counts for
13 papers · 1 filter
Multi-modal Depression Estimation based on Sub-attentional Fusion
Ping-Cheng Wei, Kunyu Peng, Alina Roitberg +3
Failure to timely diagnose and effectively treat depression leads to over 280 million people suffering from this psychological disorder worldwide. The information cues of depressio…
Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation
Jiaming Zhang, Kailun Yang, Hao Shi +7
In this paper, we address panoramic semantic segmentation which is under-explored due to two critical challenges: (1) image distortions and object deformations on panoramas; (2) la…
Trans4Map: Revisiting Holistic Bird's-Eye-View Mapping from Egocentric Images to Allocentric Semantics with Vision Transformers
Chang Chen, Jiaming Zhang, Kailun Yang +2
Humans have an innate ability to sense their surroundings, as they can extract the spatial representation from the egocentric perception and form an allocentric semantic map via sp…
A Comparative Analysis of Decision-Level Fusion for Multimodal Driver Behaviour Understanding
Alina Roitberg, Kunyu Peng, Zdravko Marinov +3
Visual recognition inside the vehicle cabin leads to safer driving and more intuitive human-vehicle interaction but such systems face substantial obstacles as they need to capture…
Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates
Alina Roitberg, Kunyu Peng, David Schneider +4
Driver observation models are rarely deployed under perfect conditions. In practice, illumination, camera placement and type differ from the ones present during training and unfore…
Indoor Navigation Assistance for Visually Impaired People via Dynamic SLAM and Panoptic Segmentation with an RGB-D Sensor
Wenyan Ou, Jiaming Zhang, Kunyu Peng +4
Exploring an unfamiliar indoor environment and avoiding obstacles is challenging for visually impaired people. Currently, several approaches achieve the avoidance of static obstacl…
Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-Learning
Xinyu Luo, Jiaming Zhang, Kailun Yang +3
Autonomous vehicles utilize urban scene segmentation to understand the real world like a human and react accordingly. Semantic segmentation of normal scenes has experienced a remar…
Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation
Jiaming Zhang, Kailun Yang, Chaoxiang Ma +3
Panoramic images with their 360-degree directional view encompass exhaustive information about the surrounding space, providing a rich foundation for scene understanding. To unfold…
MatchFormer: Interleaving Attention in Transformers for Feature Matching
Qing Wang, Jiaming Zhang, Kailun Yang +2
Local feature matching is a computationally intensive task at the subpixel level. While detector-based methods coupled with feature descriptors struggle in low-texture scenes, CNN-…
TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration
Kunyu Peng, Alina Roitberg, Kailun Yang +2
Traditional video-based human activity recognition has experienced remarkable progress linked to the rise of deep learning, but this effect was slower as it comes to the downstream…
TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation
Ruiping Liu, Kailun Yang, Alina Roitberg +5
Semantic segmentation benchmarks in the realm of autonomous driving are dominated by large pre-trained transformers, yet their widespread adoption is impeded by substantial computa…
Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
Kunyu Peng, Alina Roitberg, Kailun Yang +2
Occlusions are universal disruptions constantly present in the real world. Especially for sparse representations, such as human skeletons, a few occluded points might destroy the g…
Should I take a walk? Estimating Energy Expenditure from Video Data
Kunyu Peng, Alina Roitberg, Kailun Yang +2
We explore the problem of automatically inferring the amount of kilocalories used by human during physical activity from his/her video observation. To study this underresearched ta…