Fetching the paper…
Reading the bibliography…
Multimodal emotion recognition is an important research topic in artificial intelligence, whose main goal is to integrate multimodal clues to identify human emotional states.
Affective computing
Rosalind W Picard · 2000
Earlier work this paper cites.
Iemocap: Interactive emotional dyadic motion capture database
Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N Chang, Sungbok Lee, and Shrikanth S Narayanan · 2008
Earlier work this paper cites.
Survey on speech emotion recognition: Features, classification schemes, and databases
Moataz El Ayadi, Mohamed S Kamel, and Fakhri Karray · 2011
Earlier work this paper cites.
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
Earlier work this paper cites.
Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild
Shan Li, Weihong Deng, and JunPing Du · 2017
Earlier work this paper cites.
Affectnet: A database for facial expression, valence, and arousal computing in the wild
Ali Mollahosseini, Behzad Hasani, and Mohammad H Mahoor · 2017
Earlier work this paper cites.
Multimodal machine learning: A survey and taxonomy
Tadas Baltrušaitis, Chaitanya Ahuja, and Louis-Philippe Morency · 2018
Earlier work this paper cites.
Survey on emotional body gesture recognition
Fatemeh Noroozi, Ciprian Adrian Corneanu, Dorota Kamińska, Tomasz Sapiński, Sergio Escalera, and Gholamreza Anbarjafari · 2018
Earlier work this paper cites.
Meld: A multimodal multi-party dataset for emotion recognition in conversations
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Mihalcea · 2019
Earlier work this paper cites.
Multimodal transformer for unaligned multimodal language sequences
Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J Zico Kolter, Louis-Philippe Morency, and Ruslan Salakhutdinov · 2019
Earlier work this paper cites.
Ch-sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality
Wenmeng Yu, Hua Xu, Fanyang Meng, Yilin Zhu, Yixiao Ma, Jiele Wu, Jiyun Zou, and Kaicheng Yang · 2020
Earlier work this paper cites.
Deep facial expression recognition: A survey
Shan Li and Weihong Deng · 2020
Earlier work this paper cites.
Ctnet: Conversational transformer network for emotion recognition
Zheng Lian, Bin Liu, and Jianhua Tao · 2021
Earlier work this paper cites.
Video-based facial micro-expression analysis: A survey of datasets, features and algorithms
Xianye Ben, Yi Ren, Junping Zhang, Su-Jing Wang, Kidiyo Kpalma, Weixiao Meng, and Yong-Jin Liu · 2021
Cited alongside, same era.
Open-vocabulary object detection using captions
Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang · 2021
Cited alongside, same era.
Open-vocabulary object detection via vision and language knowledge distillation
Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui · 2021
Cited alongside, same era.
Language-driven semantic segmentation
Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl · 2021
Cited alongside, same era.
Scaling open-vocabulary image segmentation with image-level labels
Golnaz Ghiasi, Xiuye Gu, Yin Cui, and Tsung-Yi Lin · 2022
Cited alongside, same era.
Chatgpt, 2022
OpenAI · 2022
Cited alongside, same era.
Qwen-audio: Advancing universal audio understanding via unified large-scale audio-language models
Yunfei Chu, Jin Xu, Xiaohuan Zhou, Qian Yang, Shiliang Zhang, Zhijie Yan, Chang Zhou, and Jingren Zhou · 2023
Closest in time.
Onellm: One framework to align all modalities with language
Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang, Dahua Lin, Yu Qiao, Peng Gao, and Xiangyu Yue · 2023
Closest in time.
Otter: A multi-modal model with in-context instruction tuning
Bo Li, Yuanhan Zhang, Liangyu Chen, Jinghao Wang, Jingkang Yang, and Ziwei Liu · 2023
Closest in time.
Video-llama: An instruction-tuned audio-visual language model for video understanding
Hang Zhang, Xin Li, and Lidong Bing · 2023
Closest in time.
Pandagpt: One model to instruction-follow them all
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mer 2023: Multi-label learning, modality robustness, and semi-supervised learning
Zheng Lian, Haiyang Sun, Licai Sun, Kang Chen, Mngyu Xu, Kexin Wang, Ke Xu, Yu He, Ying Li, Jinming Zhao, et al · 2023
Cited alongside, same era.
Smg: A micro-gesture dataset towards spontaneous body gestures for emotional stress state analysis
Haoyu Chen, Henglin Shi, Xin Liu, Xiaobai Li, and Guoying Zhao · 2023
Cited alongside, same era.
Pla: Language-driven open-vocabulary 3d scene understanding
Runyu Ding, Jihan Yang, Chuhui Xue, Wenqing Zhang, Song Bai, and Xiaojuan Qi · 2023
Cited alongside, same era.
Videochat: Chat-centric video understanding
KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao · 2023
Cited alongside, same era.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
Cited alongside, same era.
Salmonn: Towards generic hearing abilities for large language models
Changli Tang, Wenyi Yu, Guangzhi Sun, Xianzhao Chen, Tian Tan, Wei Li, Lu Lu, Zejun MA, and Chao Zhang · 2023
Cited alongside, same era.
Yixuan Su, Tian Lan, Huayang Li, Jialu Xu, Yan Wang, and Deng Cai · 2023
Closest in time.
Video-llava: Learning united visual representation by alignment before projection
Bin Lin, Bin Zhu, Yang Ye, Munan Ning, Peng Jin, and Li Yuan · 2023
Closest in time.
Llama-vid: An image is worth 2 tokens in large language models
Yanwei Li, Chengyao Wang, and Jiaya Jia · 2023
Closest in time.
mplug-owl: Modularization empowers large language models with multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al · 2023
Closest in time.
Video-chatgpt: Towards detailed video understanding via large vision and language models
Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan · 2023
Closest in time.
Peng Jin, Ryuichi Takanobu, Caiwan Zhang, Xiaochun Cao, and Li Yuan · 2023
Closest in time.
Merbench: A unified evaluation benchmark for multimodal emotion recognition
Zheng Lian, Licai Sun, Yong Ren, Hao Gu, Haiyang Sun, Lan Chen, Bin Liu, and Jianhua Tao · 2024
Closest in time.
Towards open vocabulary learning: A survey
Jianzong Wu, Xiangtai Li, Shilin Xu, Haobo Yuan, Henghui Ding, Yibo Yang, Xia Li, Jiangning Zhang, Yunhai Tong, Xudong Jiang, et al · 2024
Closest in time.
Mvbench: A comprehensive multi-modal video understanding benchmark
Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, Limin Wang, and Yu Qiao · 2024
Closest in time.