Fetching the paper…
Reading the bibliography…
Recent advancements in Large Vision Language Models (LVLMs) have revolutionized how machines understand and generate textual responses based on visual inputs, yet they often produce "hallucinatory" outputs that misinterpret visual information, posing challenges in reliability and trustworthiness.
Ensemble methods in machine learning
Thomas G Dietterich · 2000
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.
Sequence-to-sequence learning as beam-search optimization
Sam Wiseman and Alexander M Rush · 2016
Earlier work this paper cites.
The effectiveness of data augmentation in image classification using deep learning
L Perez · 2017
Earlier work this paper cites.
Improving deep learning using generic data augmentation
Luke Taylor and Geoff Nitschke · 2017
Earlier work this paper cites.
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
Earlier work this paper cites.
Object hallucination in image captioning
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko · 2018
Earlier work this paper cites.
Test-time augmentation with uncertainty estimation for deep learning-based medical image segmentation
Guotai Wang, Wenqi Li, Michael Aertsen, Jan Deprest, Sebastien Ourselin, and Tom Vercauteren · 2018
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2019
Earlier work this paper cites.
Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Earlier work this paper cites.
A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
Earlier work this paper cites.
Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Guotai Wang, Wenqi Li, Michael Aertsen, Jan Deprest, Sébastien Ourselin, and Tom Vercauteren · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Earlier work this paper cites.
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 · 2020
Earlier work this paper cites.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Earlier work this paper cites.
Understanding test-time augmentation
Masanari Kimura · 2021
Earlier work this paper cites.
Enhancing adversarial robustness via test-time transformation ensembling
Juan C Pérez, Motasem Alfarra, Guillaume Jeanneret, Laura Rueda, Ali Thabet, Bernard Ghanem, and Pablo Arbeláez · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Better aggregation in test-time augmentation
Divya Shanmugam, Davis Blalock, Guha Balakrishnan, and John Guttag · 2021
Earlier work this paper cites.
Contrastive decoding: Open-ended text generation as optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis · 2022
Cited alongside, same era.
A-okvqa: A benchmark for visual question answering using world knowledge
Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi · 2022
Cited alongside, same era.
Memo: Test time robustness via adaptation and augmentation
Marvin Zhang, Sergey Levine, and Chelsea Finn · 2022
Cited alongside, same era.
Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou · 2023
Cited alongside, same era.
Shikra: Unleashing multimodal llm’s referential dialogue magic
Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, and Rui Zhao · 2023
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
Later among the works it cites.
Video generation models as world simulators
Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh · 2024
Closest in time.
Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi · 2024
Closest in time.
Ailin Deng, Zhirui Chen, and Bryan Hooi · 2024
Closest in time.
Multi-modal hallucination control by visual information grounding
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al · 2023
Cited alongside, same era.
Dola: Decoding by contrasting layers improves factuality in large language models
Yung-Sung Chuang, Yujia Xie, Hongyin Luo, Yoon Kim, James Glass, and Pengcheng He · 2023
Cited alongside, same era.
Detecting and preventing hallucinations in large vision language models
Anisha Gunjal, Jihan Yin, and Erhan Bas · 2023
Cited alongside, same era.
Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, and Nenghai Yu · 2023
Cited alongside, same era.
Hallucination augmented contrastive learning for multimodal large language model
Chaoya Jiang, Haiyang Xu, Mengfan Dong, Jiaxing Chen, Wei Ye, Ming Yan, Qinghao Ye, Ji Zhang, Fei Huang, and Shikun Zhang · 2023
Cited alongside, same era.
Volcano: mitigating multimodal hallucination through self-feedback guided revision
Seongyun Lee, Sue Hyun Park, Yongrae Jo, and Minjoon Seo · 2023
Cited alongside, same era.
Mitigating object hallucinations in large vision-language models through visual contrastive decoding
Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing · 2023
Cited alongside, same era.
Alessandro Favero, Luca Zancato, Matthew Trager, Siddharth Choudhary, Pramuditha Perera, Alessandro Achille, Ashwin Swaminathan, and Stefano Soatto · 2024
Closest in time.
Mme: A comprehensive evaluation benchmark for multimodal large language models
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, and Rongrong Ji · 2024
Closest in time.
Vision-language models can identify distracted driver behavior from naturalistic videos
Md Zahid Hasan, Jiajing Chen, Jiyang Wang, Mohammed Shaiqur Rahman, Ameya Joshi, Senem Velipasalar, Chinmay Hegde, Anuj Sharma, and Soumik Sarkar · 2024
Closest in time.
What if…?: Counterfactual inception to mitigate hallucination effects in large multimodal models
Junho Kim, Yeon Ju Kim, and Yong Man Ro · 2024
Closest in time.
Automated evaluation of large vision-language models on self-driving corner cases
Yanze Li, Wenhua Zhang, Kai Chen, Yanxin Liu, Pengxiang Li, Ruiyuan Gao, Lanqing Hong, Meng Tian, Xinhai Zhao, Zhenguo Li, et al · 2024
Closest in time.
Evaluation and enhancement of semantic grounding in large vision-language models
Jiaying Lu, Jinmeng Rao, Kezhen Chen, Xiaoyuan Guo, Yawen Zhang, Baochen Sun, Carl Yang, and Jie Yang · 2024
Closest in time.
Contrastive region guidance: Improving grounding in vision-language models without training
David Wan, Jaemin Cho, Elias Stengel-Eskin, and Mohit Bansal · 2024
Closest in time.
Mitigating hallucinations in large vision-language models with instruction contrastive decoding
Xintong Wang, Jingheng Pan, Liang Ding, and Chris Biemann · 2024
Closest in time.
Vadclip: Adapting vision-language models for weakly supervised video anomaly detection
Peng Wu, Xuerong Zhou, Guansong Pang, Lingru Zhou, Qingsen Yan, Peng Wang, and Yanning Zhang · 2024
Closest in time.
Pensieve: Retrospect-then-compare mitigates visual hallucination
Dingchen Yang, Bowen Cao, Guang Chen, and Changjun Jiang · 2024
Closest in time.
mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration
Qinghao Ye, Haiyang Xu, Jiabo Ye, Ming Yan, Anwen Hu, Haowei Liu, Qi Qian, Ji Zhang, and Fei Huang · 2024
Closest in time.
Less is more: Mitigating multimodal hallucination from an eos decision perspective, 2024
Zihao Yue, Liang Zhang, and Qin Jin · 2024
Closest in time.
Halle-control: Controlling object hallucination in large multimodal models, 2024
Bohan Zhai, Shijia Yang, Chenfeng Xu, Sheng Shen, Kurt Keutzer, Chunyuan Li, and Manling Li · 2024
Closest in time.
Debiasing large visual language models
Yi-Fan Zhang, Weichen Yu, Qingsong Wen, Xue Wang, Zhang Zhang, Liang Wang, Rong Jin, and Tieniu Tan · 2024
Closest in time.
Mitigating object hallucination in large vision-language models via classifier-free guidance
Linxi Zhao, Yihe Deng, Weitong Zhang, and Quanquan Gu · 2024
Closest in time.