Загрузка видео...

Не удалось загрузить видео

На главную

Introducing Agentic Object Detection! Given a text prompt like “unripe strawberries” or “Kellogg’s branded cereal” and an image, we use an agentic workflow to reason at length and detect the specified objects. No need to label any training data. Watch the video for details.

397,751 просмотров • 1 год назад •via X (Twitter)

Комментарии: 11

Фото профиля Andrew Ng
Andrew Ng1 год назад

You can also play with the demo here:

Фото профиля Breadcrumb
Breadcrumb1 год назад

Looking to automate reporting? Use AI agents to turn spreadsheets to reports in minutes without any coding.

Фото профиля Edrick🕗
Edrick🕗1 год назад

Agentic workflows for computer vision makes so much sense

Фото профиля Inforida
Inforida1 год назад

Agentic Object Detection sounds fascinating. The ability to reason without labeled data is a game changer. Imagine applying this to educational tools to enhance learning experiences, making AI-powered learning more intuitive. Keep pushing the boundaries of innovation.

Фото профиля jc_stack
jc_stack1 год назад

Have you tested this against more complex scenarios like partially occluded objects or under varied lighting conditions? Really curious about edge cases and performance degradation patterns.

Фото профиля Marian Veteanu
Marian Veteanu1 год назад

Super cool! This has lots of applications!

Фото профиля Lets go Seahawks 🇺🇦
Lets go Seahawks 🇺🇦1 год назад

i asked it to detect rectangle in batsman picture and it cant find it.

Фото профиля Lets go Seahawks 🇺🇦
Lets go Seahawks 🇺🇦1 год назад

and also, what's #23 wearing? isnt it a hat?

Фото профиля NEXUS AI Solutions
NEXUS AI Solutions1 год назад

That's fascinating! Using agentic workflows to detect objects without labeled data could revolutionize how we approach image recognition tasks. How do you think this technology could be adapted for real-time applications like autonomous vehicles?

Фото профиля Nimaano
Nimaano1 год назад

Its amazing

Фото профиля Andrew Ng
Andrew Ng1 год назад

Thanks!

Похожие видео

Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

AK

62,768 просмотров • 3 лет назад