Panoptic Segmentation: Segment Everything, Everywhere, All At Once

Panoptic Segmentation is a breakthrough technology that has the ability to segment every object with semantics, cover every pixel in the image, and support all compositions of prompts at once. The paper and GitHub repository provide more information on this technology, including a segmentation interface built with a single pre-trained model.

The GitHub repository for this technology, available at https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once, contains the demo code, pre-trained models, and dataset preparation scripts. It is recommended to download the demo code using the command:

git clone [email protected]:UX-Decoder/Segment-Everything-Everywhere-All-At-Once.git && cd Segment-Everything-Everywhere-All-At-Once/demo_code && sh run_demo.sh

However, it is worth noting that the big GB files are not included in the repository, and should be downloaded separately.

The technology uses a single pre-trained model to generate panoptic segmentation outputs, and provides a graphical user interface for visualization and interaction with the results. The model architecture is based on the EfficientNet backbone and the Swin Transformer architecture, and is trained using a combination of self-supervised and supervised learning techniques.

The paper presents several impressive examples of the technology's capabilities, including segmenting complex scenes with multiple objects, handling occlusion and partial visibility, and generalizing to unseen categories. With its impressive capabilities, there is a lot of excitement and anticipation for what the future holds for Panoptic Segmentation and its potential applications, including in Stable Diffusion and other fields.

Tags: Panoptic Segmentation, Stable Diffusion, EfficientNet, Swin Transformer

Personalize-SAM: A Training-Free Approach for Segmenting Specific Visual Concepts

Personalize-SAM is a training-free Personalization approach for Segment Anything Model (SAM). Given only a single image with a reference mask, PerSAM can segment specific visual concepts, e.g., your pet dog, within other images or videos without any training.

Personalize-SAM is based on the SAM model, which was developed by Facebook AI Research. SAM is a powerful model for segmenting arbitrary objects in images and videos. However, SAM requires a large amount of training data, which can be time-consuming … click here to read

Automating Long-form Storytelling

Long-form storytelling has always been a time-consuming and challenging task. However, with the recent advancements in artificial intelligence, it is becoming possible to automate this process. While there are some tools available that can generate text, there is still a need for contextualization and keeping track of the story's flow, which is not feasible with current token limits. However, as AI technology progresses, it may become possible to contextualize and keep track of a long-form story with a single click.

Several commenters mentioned that the … click here to read

AI-Generated Images: The New Horizon in Digital Artistry

In an era where technology is evolving at an exponential rate, AI has embarked on an intriguing journey of digital artistry. Platforms like Dreamshaper , NeverEnding Dream , and Perfect World have demonstrated an impressive capability to generate high-quality, detailed, and intricate images that push the boundaries of traditional digital design.

These AI models can take a single, simple image and upscale it, enhancing its quality and clarity. The resulting … click here to read

Exciting News: Open Orca Dataset Released!

It's a moment of great excitement for the AI community as the highly anticipated Open Orca dataset has been released. This dataset has been the talk of the town ever since the research paper was published, and now it's finally here, thanks to the dedicated efforts of the team behind it.

The Open Orca dataset holds immense potential for advancing natural language processing and AI models. It promises to bring us closer to open-source models that can compete with the likes of … click here to read

Make-It-3D: Convert 2D Images to 3D Models

Make-It-3D is a powerful tool for converting 2D images into 3D models. Developed using PyTorch, this library uses advanced algorithms to analyze 2D images and create accurate and realistic 3D models. It is a great tool for artists, designers, and hobbyists who want to create 3D models without having to start from scratch.

Make-It-3D is built on several open-source libraries, including PyTorch , TinyCUDA , click here to read

Open Chat Video Editor

Open Chat Video Editor is a free and open-source video editing tool that allows users to trim, crop, and merge videos. It is developed by SCUTlihaoyu and is available on GitHub.

With Open Chat Video Editor, users can edit videos quickly and easily. It supports various video formats, including MP4, AVI, and WMV, and allows users to export edited videos in different resolutions and bitrates.

In addition to its video editing functionality, Open Chat Video Editor also uses Stable Diffusion, a generative … click here to read

LLaVA: Large Language and Vision Assistant

The paper presents the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, the authors introduce LLaVA, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.

LLaVA demonstrates impressive multimodel chat abilities and yields an 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and … click here to read

Extending Context Size in Language Models

Language models have revolutionized the way we interact with artificial intelligence systems. However, one of the challenges faced is the limited context size that affects the model's understanding and response capabilities.

In the realm of natural language processing, attention matrices play a crucial role in determining the influence of each token within a given context. This cross-correlation matrix, often represented as an NxN matrix, affects the overall model size and performance.

One possible approach to overcome the context size limitation … click here to read

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