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SqueezeNext: Hardware-Aware Neural Network Design.

, , , , , , , and . CVPR Workshops, page 1638-1647. Computer Vision Foundation / IEEE Computer Society, (2018)

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ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation., , , , , , , and . AAAI, page 3500-3509. AAAI Press, (2021)FP-NAS: Fast Probabilistic Neural Architecture Search., , , , , and . CVPR, page 15139-15148. Computer Vision Foundation / IEEE, (2021)Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs., , , , , , , , , and 1 other author(s). CoRR, (2018)Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP., , , , , , , , and . CVPR, page 7061-7070. IEEE, (2023)Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models., , , , , , , , , and . ECCV (37), volume 13697 of Lecture Notes in Computer Science, page 638-656. Springer, (2022)SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud., , , and . ICRA, page 1887-1893. IEEE, (2018)SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud., , , , and . ICRA, page 4376-4382. IEEE, (2019)Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions., , , , , , , , and . CVPR, page 9127-9135. Computer Vision Foundation / IEEE Computer Society, (2018)ControlRoom3D: Room Generation Using Semantic Proxy Rooms., , , , , , , , , and 4 other author(s). CVPR, page 6201-6210. IEEE, (2024)Co-design of deep neural nets and neural net accelerators for embedded vision applications., , , , , and . DAC, page 148:1-148:6. ACM, (2018)