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Recently, transformer-based networks have achieved state-of-the-art performance in computer vision tasks. In this paper, we propose a new cascaded MTL transformer-based framework, termed MTL-TransMODS, that tackles the moving object detection and segmentation tasks due to its importance for Autonomous Driving tasks. A critical problem in this task is how to model the spatial correlation in each frame and the temporal relationship across multiple frames to capture the motion cues. MTL-TransMODS, introducing a vision transformer to employ the temporal and spatial associations, and tackle both tasks using only one fully shared transformer architecture with unified queries. Extensive experiments demonstrate the superiority of our MTL-TransMODS over state-of-the-art methods on the KittiMoSeg dataset \cite{rashed2019fusemodnet}. Results show 0.3\% mAP improvement for Moving Object Detection, and 5.7\% IoU improvement for Moving Object Segmentation, over the state-of-the-art techniques.
Author Information
Eslam MOHAMED-ABDELRAHMAN (Valeo (F22 Building, Smart Village، Cairo - Alexandria Desert Rd, Giza))
Ahmad El Sallab (Valeo)
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Changhao Chen · Nils Gählert · Edouard Leurent · Johannes Lehner · Apratim Bhattacharyya · Harkirat Singh Behl · Teck Yian Lim · Shiho Kim · Jelena Novosel · Błażej Osiński · Arindam Das · Ruobing Shen · Jeffrey Hawke · Joachim Sicking · Babak Shahian Jahromi · Theja Tulabandhula · Claudio Michaelis · Evgenia Rusak · WENHANG BAO · Hazem Rashed · JP Chen · Amin Ansari · Jaekwang Cha · Mohamed Zahran · Daniele Reda · Jinhyuk Kim · Kim Dohyun · Ho Suk · Junekyo Jhung · Alexander Kister · Matthias Fahrland · Adam Jakubowski · Piotr Miłoś · Jean Mercat · Bruno Arsenali · Silviu Homoceanu · Xiao-Yang Liu · Philip Torr · Ahmad El Sallab · Ibrahim Sobh · Anurag Arnab · Krzysztof Galias