🔥Model Inversion Attack ToolBox v2.0🔥

🖥 Github: https://github.com/ffhibnese/model-inversion-attack-toolbox

📕 Paper: https://arxiv.org/abs/2410.05159v1

🤗 Dataset: https://paperswithcode.com/dataset/ffhq

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SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree

🖥 Github: https://github.com/mark12ding/sam2long

📕 Paper: https://arxiv.org/abs/2410.16268v1

🤗 HF: https://huggingface.co/papers/2410.16268

Don’t sleep on Vision Language Models (VLMs).

With the releases of Llama 3.2 and ColQwen2, multimodal models are gaining more and more traction.

VLMs are multimodal models that can handle image and text modalities:

Input: Image and text
Output: Text

They can be used for many use cases, including visual question answering or document understanding (as in the case of ColQwen2).

How do they work under the hood?

The main challenge in VLMs is to unify the image and text representations.

For this, a typical VLM architecture consists of the following components:

• image encoder (e.g., CLIP, SigLIP)
• embedding projector to align image and text representations
• text decoder (e.g., Vicuna, Gemma)

huggingface.co/blog/vlms

https://t.iss.one/DataScienceT

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