暂无分享,去创建一个
Ryan Cotterell | Desmond Elliott | Naoaki Okazaki | Emanuele Bugliarello | Naoaki Okazaki | Desmond Elliott | Ryan Cotterell | Emanuele Bugliarello
[1] Hugo Larochelle,et al. GuessWhat?! Visual Object Discovery through Multi-modal Dialogue , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Michael S. Bernstein,et al. Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations , 2016, International Journal of Computer Vision.
[3] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[4] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[5] Jianlong Fu,et al. Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers , 2020, ArXiv.
[6] Mohit Bansal,et al. LXMERT: Learning Cross-Modality Encoder Representations from Transformers , 2019, EMNLP.
[7] Lin Su,et al. ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data , 2020, ArXiv.
[8] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[9] Ali Farhadi,et al. Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping , 2020, ArXiv.
[10] Rico Sennrich,et al. Neural Machine Translation of Rare Words with Subword Units , 2015, ACL.
[11] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[12] Jianfeng Gao,et al. Unified Vision-Language Pre-Training for Image Captioning and VQA , 2020, AAAI.
[13] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Svetlana Lazebnik,et al. Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[15] Lei Zhang,et al. Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[16] Yoav Artzi,et al. A Corpus for Reasoning about Natural Language Grounded in Photographs , 2018, ACL.
[17] Alan L. Yuille,et al. Generation and Comprehension of Unambiguous Object Descriptions , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Christopher D. Manning,et al. GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Michael S. Bernstein,et al. Visual7W: Grounded Question Answering in Images , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[21] Cho-Jui Hsieh,et al. VisualBERT: A Simple and Performant Baseline for Vision and Language , 2019, ArXiv.
[22] Licheng Yu,et al. MAttNet: Modular Attention Network for Referring Expression Comprehension , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[23] Stefan Carlsson,et al. CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[24] Hao Tian,et al. ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph , 2020, ArXiv.
[25] Asim Kadav,et al. Visual Entailment: A Novel Task for Fine-Grained Image Understanding , 2019, ArXiv.
[26] Margaret Mitchell,et al. VQA: Visual Question Answering , 2015, International Journal of Computer Vision.
[27] Radu Soricut,et al. Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning , 2018, ACL.
[28] Furu Wei,et al. VL-BERT: Pre-training of Generic Visual-Linguistic Representations , 2019, ICLR.
[29] Vicente Ordonez,et al. ReferItGame: Referring to Objects in Photographs of Natural Scenes , 2014, EMNLP.
[30] Jianfeng Gao,et al. Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks , 2020, ECCV.
[31] Nan Duan,et al. Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training , 2019, AAAI.
[32] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Yu Cheng,et al. UNITER: UNiversal Image-TExt Representation Learning , 2019, ECCV.
[34] Ali Farhadi,et al. From Recognition to Cognition: Visual Commonsense Reasoning , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Andrew McCallum,et al. Energy and Policy Considerations for Deep Learning in NLP , 2019, ACL.
[36] Marcus Rohrbach,et al. 12-in-1: Multi-Task Vision and Language Representation Learning , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Stefan Lee,et al. ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks , 2019, NeurIPS.
[38] Yash Goyal,et al. Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Jingren Zhou,et al. InterBERT: An Effective Multi-Modal Pretraining Approach via Vision-and-Language Interaction , 2020 .
[40] Geoffrey E. Hinton,et al. Layer Normalization , 2016, ArXiv.