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We propose InstructDET, a data-centric method for referring object detection (ROD) that localizes target objects based on user instructions.
A density-based algorithm for discovering clusters in large spatial databases with noise
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
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Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy · 2016
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
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Multi-level multimodal common semantic space for image-phrase grounding
Hassan Akbari, Svebor Karaman, Surabhi Bhargava, Brian Chen, Carl Vondrick, and Shih-Fu Chang · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Objects365: A large-scale, high-quality dataset for object detection
Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun · 2019
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End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
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Cops-ref: A new dataset and task on compositional referring expression comprehension
Zhenfang Chen, Peng Wang, Lin Ma, Kwan-Yee K Wong, and Qi Wu · 2020
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The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
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Vl-bert: Pre-training of generic visual-linguistic representations
Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai · 2020
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Deformable detr: Deformable transformers for end-to-end object detection
Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai · 2020
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Grounding physical concepts of objects and events through dynamic visual reasoning
Zhenfang Chen, Jiayuan Mao, Jiajun Wu, Kwan-Yee Kenneth Wong, Joshua B. Tenenbaum, and Chuang Gan · 2021
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Transvg: End-to-end visual grounding with transformers
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, et al · 2022
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Seqtr: A simple yet universal network for visual grounding
Chaoyang Zhu, Yiyi Zhou, Yunhang Shen, Gen Luo, Xingjia Pan, Mingbao Lin, Chao Chen, Liujuan Cao, Xiaoshuai Sun, and Rongrong Ji · 2022
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A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui · 2023
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Learning to jointly share and prune weights for grounding based vision and language models
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Soft neighbors are positive supporters in contrastive visual representation learning
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Mdetr–modulated detection for end-to-end multi-modal understanding
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A spoken language dataset of descriptions for speech-based grounded language learning
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Referring transformer: A one-step approach to multi-task visual grounding
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Learning transferable visual models from natural language supervision
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