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Ferret: Refer and Ground Anything Anywhere at Any Granularity.

, , , , , , , , и . ICLR, OpenReview.net, (2024)

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Xianzhi Xiong

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From Scarcity to Efficiency: Improving CLIP Training via Visual-enriched Captions., , , , , , , , , и 1 other автор(ы). CoRR, (2023)Computer Vision and Deep Learning with Applications to Object Detection, Segmentation, and Document Analysis.. University of Maryland, College Park, MD, USA, (2017)base-search.net (ftunivmaryland:oai:drum.lib.umd.edu:1903/20758).AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts., , , , , , , и . ICCV, стр. 17300-17311. IEEE, (2023)A graphical model approach for matching partial signatures., , и . CVPR, стр. 1465-1472. IEEE Computer Society, (2015)Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection., , , и . CoRR, (2016)Efficient Scale-Permuted Backbone with Learned Resource Distribution., , , , , , и . ECCV (23), том 12368 из Lecture Notes in Computer Science, стр. 572-586. Springer, (2020)Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance., , , , , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 25760-25782. PMLR, (2022)Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts., , , , , , , , , и . CoRR, (2023)Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation., , , , , и . NeurIPS, (2022)SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization., , , , , , , и . CoRR, (2019)