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Contrastive Language-Image Pre-training (CLIP) represents the latest incarnation of pre-trained vision-language models.
An argument for basic emotions
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Emotional category data on images from the international affective picture system
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Extracting moods from pictures and sounds: Towards truly personalized tv
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Imagenet: A large-scale hierarchical image database
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Affective image classification using features inspired by psychology and art theory
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Imagenet classification with deep convolutional neural networks
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Large-scale visual sentiment ontology and detectors using adjective noun pairs
Damian Borth, Rongrong Ji, Tao Chen, Thomas Breuel, and Shih-Fu Chang · 2013
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Categorical and dimensional affect analysis in continuous input: Current trends and future directions
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Findings of the 2014 workshop on statistical machine translation
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Deepsentibank: Visual sentiment concept classification with deep convolutional neural networks
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Exploring principles-of-art features for image emotion recognition
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Spatial transformer networks
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Robust image sentiment analysis using progressively trained and domain transferred deep networks
Quanzeng You, Jiebo Luo, Hailin Jin, and Jianchao Yang · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Where do emotions come from? predicting the emotion stimuli map
Kuan-Chuan Peng, Amir Sadovnik, Andrew Gallagher, and Tsuhan Chen · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Building a large scale dataset for image emotion recognition: The fine print and the benchmark
Quanzeng You, Jiebo Luo, Hailin Jin, and Jianchao Yang · 2016
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Predicting personalized emotion perceptions of social images
Sicheng Zhao, Hongxun Yao, Yue Gao, Rongrong Ji, Wenlong Xie, Xiaolei Jiang, and Tat-Seng Chua · 2016
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Residual attention network for image classification
Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang · 2017
Learning multi-level deep representations for image emotion classification
Tianrong Rao, Xiaoxu Li, and Min Xu · 2020
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How neural networks extrapolate: From feedforward to graph neural networks
Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2020
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Weakly supervised emotion intensity prediction for recognition of emotions in images
Haimin Zhang and Min Xu · 2020
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, and Furu Wei · 2021
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Emotion class-wise aware loss for image emotion classification
Sinuo Deng, Lifang Wu, Ge Shi, Heng Zhang, Wenjin Hu, and Ruihai Dong · 2021
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Joint image emotion classification and distribution learning via deep convolutional neural network
Jufeng Yang, Dongyu She, and Ming Sun · 2017
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Dependency exploitation: A unified cnn-rnn approach for visual emotion recognition
Xinge Zhu, Liang Li, Weigang Zhang, Tianrong Rao, Min Xu, Qingming Huang, and Dong Xu · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Weakly supervised coupled networks for visual sentiment analysis
Jufeng Yang, Dongyu She, Yu-Kun Lai, Paul L Rosin, and Ming-Hsuan Yang · 2018
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Visual sentiment prediction based on automatic discovery of affective regions
Jufeng Yang, Dongyu She, Ming Sun, Ming-Ming Cheng, Paul L Rosin, and Liang Wang · 2018
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Lucfer: A large-scale context-sensitive image dataset for deep learning of visual emotions
Pooyan Balouchian, Marjaneh Safaei, and Hassan Foroosh · 2019
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Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, and Jun Zhu · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Discovering sentimental interaction via graph convolutional network for visual sentiment prediction
Lifang Wu, Heng Zhang, Sinuo Deng, Ge Shi, and Xu Liu · 2021
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Affective image content analysis: Two decades review and new perspectives
Sicheng Zhao, Xingxu Yao, Jufeng Yang, Guoli Jia, Guiguang Ding, Tat-Seng Chua, Bjoern W Schuller, and Kurt Keutzer · 2021
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2021
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Image sentiment classification via multi-level sentiment region correlation analysis
Jing Zhang, Xinyu Liu, Mei Chen, Qi Ye, and Zhe Wang · 2022
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Computational emotion analysis from images: Recent advances and future directions
Sicheng Zhao, Quanwei Huang, Youbao Tang, Xingxu Yao, Jufeng Yang, Guiguang Ding, and Björn W Schuller · 2022
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