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CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow
March 15, 2024, 4:45 a.m. | Chenbin Pan, Burhaneddin Yaman, Senem Velipasalar, Liu Ren
cs.CV updates on arXiv.org arxiv.org
Abstract: Autonomous driving stands as a pivotal domain in computer vision, shaping the future of transportation. Within this paradigm, the backbone of the system plays a crucial role in interpreting the complex environment. However, a notable challenge has been the loss of clear supervision when it comes to Bird's Eye View elements. To address this limitation, we introduce CLIP-BEVFormer, a novel approach that leverages the power of contrastive learning techniques to enhance the multi-view image-derived BEV …
abstract arxiv autonomous autonomous driving challenge clear clip computer computer vision cs.cv domain driving environment flow future however image loss paradigm pivotal role supervision transportation truth type view vision
More from arxiv.org / cs.CV updates on arXiv.org
Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs
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