On Distinctive Image Captioning via Comparing and Reweighting
暂无分享,去创建一个
[1] Ilya Sutskever,et al. Learning Transferable Visual Models From Natural Language Supervision , 2021, ICML.
[2] Chen Change Loy,et al. FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[3] Stefan Roth,et al. Diverse Image Captioning with Context-Object Split Latent Spaces , 2020, NeurIPS.
[4] Siddharth Dalmia,et al. On Long-Tailed Phenomena in Neural Machine Translation , 2020, FINDINGS.
[5] Antoni B. Chan,et al. On Diversity in Image Captioning: Metrics and Methods , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Yu Wang,et al. Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets , 2020, ECCV.
[7] Antoni B. Chan,et al. Compare and Reweight: Distinctive Image Captioning Using Similar Images Sets , 2020, ECCV.
[8] Quan Hung Tran,et al. Context-Aware Group Captioning via Self-Attention and Contrastive Features , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Junjie Yan,et al. Equalization Loss for Long-Tailed Object Recognition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Fawaz Sammani,et al. Show, Edit and Tell: A Framework for Editing Image Captions , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Gal Oren,et al. Joint Optimization for Cooperative Image Captioning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[12] Xiaojun Wan,et al. Generating Diverse and Descriptive Image Captions Using Visual Paraphrases , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[13] Tao Mei,et al. Hierarchy Parsing for Image Captioning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[14] Dhruv Batra,et al. Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[15] Jie Chen,et al. Attention on Attention for Image Captioning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[16] Antoni B. Chan,et al. Towards Diverse and Accurate Image Captions via Reinforcing Determinantal Point Process , 2019, ArXiv.
[17] Stefan Lee,et al. ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks , 2019, NeurIPS.
[18] Cesc Chunseong Park,et al. Towards Personalized Image Captioning via Multimodal Memory Networks , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Antoni B. Chan,et al. Describing Like Humans: On Diversity in Image Captioning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Qi Xie,et al. Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting , 2019, NeurIPS.
[21] Yang Song,et al. Class-Balanced Loss Based on Effective Number of Samples , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Jianfei Cai,et al. Auto-Encoding Scene Graphs for Image Captioning , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Qiang Liu,et al. Neural Image Caption Generation with Weighted Training and Reference , 2018, Cognitive Computation.
[24] Piek T. J. M. Vossen,et al. Measuring the Diversity of Automatic Image Descriptions , 2018, COLING.
[25] Wei Liu,et al. Recurrent Fusion Network for Image Captioning , 2018, ECCV.
[26] Henning Müller,et al. Overview of the ImageCLEF 2018 Caption Prediction Tasks , 2018, CLEF.
[27] Rongrong Ji,et al. GroupCap: Group-Based Image Captioning with Structured Relevance and Diversity Constraints , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[28] Alexander Schwing,et al. Fast, Diverse and Accurate Image Captioning Guided by Part-Of-Speech , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Antoni B. Chan,et al. CNN+CNN: Convolutional Decoders for Image Captioning , 2018, CVPR 2018.
[30] Chen Chen,et al. Improving Image Captioning with Conditional Generative Adversarial Nets , 2018, AAAI.
[31] Lei Zhang,et al. Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning , 2018, ArXiv.
[32] Xiaogang Wang,et al. Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially Labeled Data , 2018, ECCV.
[33] Xi Chen,et al. Stacked Cross Attention for Image-Text Matching , 2018, ECCV.
[34] Gregory Shakhnarovich,et al. Discriminability Objective for Training Descriptive Captions , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[35] Alexander G. Schwing,et al. Convolutional Image Captioning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[36] Gang Wang,et al. Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[37] Bo Dai,et al. Contrastive Learning for Image Captioning , 2017, NIPS.
[38] Gang Wang,et al. Stack-Captioning: Coarse-to-Fine Learning for Image Captioning , 2017, AAAI.
[39] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[40] Kaiming He,et al. Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[41] 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.
[42] David J. Fleet,et al. VSE++: Improving Visual-Semantic Embeddings with Hard Negatives , 2017, BMVC.
[43] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[44] Gunhee Kim,et al. Attend to You: Personalized Image Captioning with Context Sequence Memory Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Bernt Schiele,et al. Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[46] Ross B. Girshick,et al. Mask R-CNN , 2017, 1703.06870.
[47] Sanja Fidler,et al. Towards Diverse and Natural Image Descriptions via a Conditional GAN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[48] Samy Bengio,et al. Context-Aware Captions from Context-Agnostic Supervision , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Tao Mei,et al. Boosting Image Captioning with Attributes , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[50] Basura Fernando,et al. SPICE: Semantic Propositional Image Caption Evaluation , 2016, ECCV.
[51] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[52] 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.
[53] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[54] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[55] Wei Xu,et al. Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) , 2014, ICLR.
[56] Fei-Fei Li,et al. Deep visual-semantic alignments for generating image descriptions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[57] C. Lawrence Zitnick,et al. CIDEr: Consensus-based image description evaluation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Samy Bengio,et al. Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[60] Alon Lavie,et al. Meteor Universal: Language Specific Translation Evaluation for Any Target Language , 2014, WMT@ACL.
[61] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[62] Cyrus Rashtchian,et al. Every Picture Tells a Story: Generating Sentences from Images , 2010, ECCV.
[63] Chin-Yew Lin,et al. ROUGE: A Package for Automatic Evaluation of Summaries , 2004, ACL 2004.
[64] Salim Roukos,et al. Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.
[65] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[66] A. Linear-probe,et al. Learning Transferable Visual Models From Natural Language Supervision , 2021 .
[67] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[68] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[69] Pratik Rane,et al. Self-Critical Sequence Training for Image Captioning , 2018 .
[70] Xu Sun,et al. Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation , 2018, EMNLP.
[71] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .
[72] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..