VirTex: Learning Visual Representations from Textual Annotations
The authors offer an alternative approach to pre-training backbones for CV tasks – using semantically dense captions to learn visual representations.
Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, the authors aim to learn high-quality visual representations from fewer images. They revisit supervised pretraining and seek data-efficient alternatives to classification-based pretraining.
VirTex (CNN + Transformer) is pre-trained on COCO captions. On downstream tasks it can reach performance similar to pre-training on ImageNet, but with 10x less images!
Paper: https://arxiv.org/abs/2006.06666
Code: https://github.com/kdexd/virtex
Site: https://kdexd.github.io/virtex/
#imagecaptioning #cv #visual #annotation #transformer #pretraining #transferlearning #deeplearning #paper
The authors offer an alternative approach to pre-training backbones for CV tasks – using semantically dense captions to learn visual representations.
Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, the authors aim to learn high-quality visual representations from fewer images. They revisit supervised pretraining and seek data-efficient alternatives to classification-based pretraining.
VirTex (CNN + Transformer) is pre-trained on COCO captions. On downstream tasks it can reach performance similar to pre-training on ImageNet, but with 10x less images!
Paper: https://arxiv.org/abs/2006.06666
Code: https://github.com/kdexd/virtex
Site: https://kdexd.github.io/virtex/
#imagecaptioning #cv #visual #annotation #transformer #pretraining #transferlearning #deeplearning #paper
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
ChatGPT is a language interface with distinctive conversational competency and reasoning capabilities across many domains. However, it is currently unable to process or generate images from the visual world. To address this limitation, the authors propose a system called Visual ChatGPT that incorporates different Visual Foundation Models to enable users to interact with ChatGPT using both language and images. The system is capable of handling complex visual questions or instructions that require multiple AI models and steps. Additionally, it allows for feedback and corrections.
Rather than creating a new multimodal ChatGPT from scratch, the authors propose building Visual ChatGPT by incorporating various (22) Visual Foundation Models (VFMs) directly into ChatGPT. To facilitate the integration of these VFMs, the authors introduce a Prompt Manager that supports several functions. These include specifying the input-output formats of each VFM, converting visual information to language format, and managing the histories, priorities, and conflicts of different VFMs. With the Prompt Manager's help, ChatGPT can use these VFMs iteratively and receive their feedback until it satisfies the users' requirements or reaches the end condition.
Paper: https://arxiv.org/abs/2303.04671
Code link: https://github.com/microsoft/visual-chatgpt
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-palme
#deeplearning #nlp #transformer #sota #languagemodel #visual
ChatGPT is a language interface with distinctive conversational competency and reasoning capabilities across many domains. However, it is currently unable to process or generate images from the visual world. To address this limitation, the authors propose a system called Visual ChatGPT that incorporates different Visual Foundation Models to enable users to interact with ChatGPT using both language and images. The system is capable of handling complex visual questions or instructions that require multiple AI models and steps. Additionally, it allows for feedback and corrections.
Rather than creating a new multimodal ChatGPT from scratch, the authors propose building Visual ChatGPT by incorporating various (22) Visual Foundation Models (VFMs) directly into ChatGPT. To facilitate the integration of these VFMs, the authors introduce a Prompt Manager that supports several functions. These include specifying the input-output formats of each VFM, converting visual information to language format, and managing the histories, priorities, and conflicts of different VFMs. With the Prompt Manager's help, ChatGPT can use these VFMs iteratively and receive their feedback until it satisfies the users' requirements or reaches the end condition.
Paper: https://arxiv.org/abs/2303.04671
Code link: https://github.com/microsoft/visual-chatgpt
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-palme
#deeplearning #nlp #transformer #sota #languagemodel #visual
