Visual Instruction Tuning
LLaVA paper: align LLMs with visual information through instruction tuning on image-text pairs, enabling multimodal understanding and reasoning.
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LLaVA paper: align LLMs with visual information through instruction tuning on image-text pairs, enabling multimodal understanding and reasoning.
Investigating the effectiveness of plain Vision Transformers as backbones for object detection and proposing modifications to improve their performance.
Introducing YOLO, a unified, real-time object detection system that frames object detection as a single regression problem.
Introducing EfficientNet, a family of convolutional neural networks that achieve state-of-the-art accuracy with significantly improved efficiency through a novel compound scaling method.
Faster R-CNN explained: how Region Proposal Networks (RPN) enable near real-time object detection with shared convolutional features.
Introducing SAM (Segment Anything), a promptable segmentation model capable of segmenting any object in an image with a wide range of prompts, including points, boxes, and text.