SecondShiftAugie
Personalize-SAM
SecondShiftAugie | Personalize-SAM | |
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6 | 11 | |
11 | 1,437 | |
- | - | |
6.5 | 7.1 | |
about 1 year ago | 9 months ago | |
Python | Python | |
MIT License | MIT License |
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SecondShiftAugie
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Weekly summary - 21 May 2023
https://github.com/AugustWasilowski/SecondShiftAugie created by u/MayorAwesome
- Weekly Megathread - 14 May 2023
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Weekly Megathread
https://github.com/AugustWasilowski/SecondShiftAugie - Second Shift Augie is a sassy and sarcastic AI assistant that helps answer questions and summarize YouTube videos. It uses the discord.py library for interacting with the Discord API and has several different features, including text-to-speech functionality. Made by u/MayorAwesome
- Second Shift Augie: AI-Packed Discord Bot, Open source project, looking for contributors and testers.
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How to add slash commands to code running inside cogs?
This is for my AI chatbot Second Shift Augie. Here's the repo: https://github.com/AugustWasilowski/SecondShiftAugie
- I made a discord bot that connects to ChatGPT, Wolfram Alpha, SerapApi, and can summarize YouTube videos. Anyone want to help me develop it?
Personalize-SAM
- Weekly Megathread - 14 May 2023
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This AI Research Proposes PerSAM: A Training-Free Personalization Approach For The Segment Anything Model (SAM)
Code: https://github.com/ZrrSkywalker/Personalize-SAM
- GitHub - ZrrSkywalker/Personalize-SAM: Personalize Segment Anything Model (SAM) with 1 shot in 10 seconds
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Personalize Segment Anything Model with One Shot
Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models. Despite the generality, customizing SAM for specific visual concepts without man-powered prompting is under explored, e.g., automatically segmenting your pet dog in different images. In this paper, we propose a training-free Personalization approach for SAM, termed as PerSAM. Given only a single image with a reference mask, PerSAM first localizes the target concept by a location prior, and segments it within other images or videos via three techniques: target-guided attention, target-semantic prompting, and cascaded post-refinement. In this way, we effectively adapt SAM for private use without any training. To further alleviate the mask ambiguity, we present an efficient one-shot fine-tuning variant, PerSAM-F. Freezing the entire SAM, we introduce two learnable weights for multi-scale masks, only training 2 parameters within 10 seconds for improved performance. To demonstrate our efficacy, we construct a new segmentation dataset, PerSeg, for personalized evaluation, and test our methods on video object segmentation with competitive performance. Besides, our approach can also enhance DreamBooth to personalize Stable Diffusion for text-to-image generation, which discards the background disturbance for better target appearance learning. Code is released at https://github.com/ZrrSkywalker/Personalize-SAM
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