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Large Vision-Language Models (LVLMs) can understand the world comprehensively by integrating rich information from different modalities, achieving remarkable advancements on various multimodal downstream tasks.
Optimal brain damage
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Imagenet: A large-scale hierarchical image database
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Sun database: Large-scale scene recognition from abbey to zoo
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Cats and dogs
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Describing textures in the wild
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
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Food-101 - mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei · 2014
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Microsoft coco captions: Data collection and evaluation server
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
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Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2016
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Eie: Efficient inference engine on compressed deep neural network
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Multimodal residual learning for visual qa
Jin-Hwa Kim, Sang-Woo Lee, Donghyun Kwak, Min-Oh Heo, Jeonghee Kim, Jung-Woo Ha, and Byoung-Tak Zhang · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas R. Dengel, and Damian Borth · 2017
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Combined group and exclusive sparsity for deep neural networks
Jaehong Yoon and Sung Ju Hwang · 2017
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Shallowing deep networks: Layer-wise pruning based on feature representations
Shi Chen and Qi Zhao · 2018
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Compressing neural networks using the variational information bottleneck
Bin Dai, Chen Zhu, Baining Guo, and David Wipf · 2018
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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Learning visual and textual representations for multimodal matching and classification
Yu Liu, Li Liu, Yanming Guo, and Michael S Lew · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
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Improving language understanding by generative pre-training
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
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nocaps: novel object captioning at scale
Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, and Peter Anderson · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A. Hudson and Christopher D. Manning · 2019
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Token merging: Your vit but faster
Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, and Judy Hoffman · 2022
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Scaling instruction-finetuned language models
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Coded sparse matrix computation schemes that leverage partial stragglers
Anindya Bijoy Das and Aditya Ramamoorthy · 2022
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The forward-forward algorithm: Some preliminary investigations
Geoffrey E. Hinton · 2022
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Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi · 2019
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Structured pruning of a bert-based question answering model
JS McCarley, Rishav Chakravarti, and Avirup Sil · 2019
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Importance estimation for neural network pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, and Jan Kautz · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam M. Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2019
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A corpus for reasoning about natural language grounded in photographs
Alane Suhr, Stephanie Zhou, Iris Zhang, Huajun Bai, and Yoav Artzi · 2019
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Structured pruning of large language models
Ziheng Wang, Jeremy Wohlwend, and Tao Lei · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Spvit: Enabling faster vision transformers via latency-aware soft token pruning
Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Wei Niu, Mengshu Sun, Xuan Shen, Geng Yuan, Bin Ren, Hao Tang, et al · 2022
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A fast post-training pruning framework for transformers
Woosuk Kwon, Sehoon Kim, Michael W Mahoney, Joseph Hassoun, Kurt Keutzer, and Amir Gholami · 2022
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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Deepsparse
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Bitat: Neural network binarization with task-dependent aggregated transformation
Geon Park, Jaehong Yoon, Haiyang Zhang, Xing Zhang, Sung Ju Hwang, and Yonina C Eldar · 2022
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Make-a-video: Text-to-video generation without text-video data
Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al · 2022
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Token merging for fast stable diffusion
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Sparsegpt: Massive language models can be accurately pruned in one-shot
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Text-conditioned sampling framework for text-to-image generation with masked generative models
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LAVIS: A one-stop library for language-vision intelligence
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Fine-tuning language models with just forward passes
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Upop: Unified and progressive pruning for compressing vision-language transformers
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Llama: Open and efficient foundation language models
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Joint token pruning and squeezing towards more aggressive compression of vision transformers
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Smoothquant: Accurate and efficient post-training quantization for large language models
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mplug-owl: Modularization empowers large language models with multimodality
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