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20202024
most citedTransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation

27 citations · 127 across the 53 of their papers we have counts for

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Showing 2022Show all

12 papers · 1 filter

cs.CV2022

Computational Imaging for Machine Perception: Transferring Semantic Segmentation beyond Aberrations

Qi Jiang, Hao Shi, Shaohua Gao +5

Semantic scene understanding with Minimalist Optical Systems (MOS) in mobile and wearable applications remains a challenge due to the corrupted imaging quality induced by optical a…

cs.CV2022★ 1 cited

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…

cs.CV2022★ 12 cited

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…

cs.CV2022

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…

cs.CV2022★ 3 cited

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…

cs.CV2022

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…

cs.CV2022

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…

cs.CV2022★ 1 cited

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-…

cs.CV2022★ 5 cited

CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers

Jiaming Zhang, Huayao Liu, Kailun Yang +3

Scene understanding based on image segmentation is a crucial component of autonomous vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting compleme…

cs.CV2022★ 1 cited

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…

cs.CV2022★ 27 cited

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…

cs.CV2022

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…