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This paper investigates an under-explored but important problem: given a collection of pre-trained neural networks, predicting their performance on each multi-modal task without fine-tuning them, such as image recognition, referring, captioning, visual question answering, and text question answering.
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Vqa: Visual question answering
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Mengye Ren, Ryan Kiros, and Richard Zemel · 2015
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Microsoft coco captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick · 2015
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Dynamic memory networks for visual and textual question answering
Caiming Xiong, Stephen Merity, and Richard Socher · 2016
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Yfcc100m: The new data in multimedia research
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Deep residual learning for image recognition
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Modeling context in referring expressions
Licheng Yu, Patrick Poirson, Shan Yang, Alexander C Berg, and Tamara L Berg · 2016
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Generation and comprehension of unambiguous object descriptions
Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy · 2016
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Electra: Pre-training text encoders as discriminators rather than generators
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Deberta: Decoding-enhanced bert with disentangled attention
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Rethinking the hyperparameters for fine-tuning
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Stylenet: Generating attractive visual captions with styles
Chuang Gan, Zhe Gan, Xiaodong He, Jianfeng Gao, and Li Deng · 2017
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
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Clip-adapter: Better vision-language models with feature adapters
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Emerging properties in self-supervised vision transformers
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An empirical study of training self-supervised visual transformers. arxiv e-prints
X Chen, S Xie, and K He · 2021
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Align before fuse: Vision and language representation learning with momentum distillation
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Lavt: Language-aware vision transformer for referring image segmentation
Zhao Yang, Jiaqi Wang, Yansong Tang, Kai Chen, Hengshuang Zhao, and Philip HS Torr · 2022
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Not all models are equal: Predicting model transferability in a self-challenging fisher space
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Frustratingly easy transferability estimation
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Pactran: Pac-bayesian metrics for estimating the transferability of pretrained models to classification tasks
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Transferability estimation using bhattacharyya class separability
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Uni-perceiver: Pre-training unified architecture for generic perception for zero-shot and few-shot tasks
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Flava: A foundational language and vision alignment model
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Altclip: Altering the language encoder in clip for extended language capabilities
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Swin transformer v2: Scaling up capacity and resolution
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Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework
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Multimodality helps unimodality: Cross-modal few-shot learning with multimodal models
Zhiqiu Lin, Samuel Yu, Zhiyi Kuang, Deepak Pathak, and Deva Ramana · 2023
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