2023
Visual Instruction Tuning
Introducing a method for aligning large language models (LLMs) with visual information by instruction tuning on a massive dataset of image-text pairs.
Tagged with
deep learning
Explore machine learning papers and reviews related to deep learning. Find insights, analysis, and implementation details.
15
Papers Found
Papers Related to deep learning
2022
Exploring Plain Vision Transformer Backbones for Object Detection
Investigating the effectiveness of plain Vision Transformers as backbones for object detection and proposing modifications to improve their performance.
2016
You Only Look Once: Unified, Real-Time Object Detection
Introducing YOLO, a unified, real-time object detection system that frames object detection as a single regression problem.
2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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.
2015
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Introducing Faster R-CNN, a significant improvement over R-CNN and Fast R-CNN that uses a Region Proposal Network (RPN) to generate object proposals, leading to faster and more accurate object detection.
2023
Segment Anything
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.
2020
End-to-End Object Detection with Transformers
Introducing DETR, a novel end-to-end object detection framework that leverages Transformers to directly predict a set of object bounding boxes.
2023
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
Computer VisionNatural Language ProcessingDeep LearningMultimodal LearningBLIP-2Vision-Language Models
Introducing BLIP-2, a new vision-language model that leverages frozen image encoders and large language models to achieve improved efficiency and performance in various multimodal tasks.
2021
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Introducing Vision Transformer (ViT), a pure transformer architecture for image recognition that achieves state-of-the-art results.
2021
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Introducing Swin Transformer, a hierarchical Vision Transformer that uses shifted windows to achieve improved efficiency and performance in various vision tasks.
2021
Learning Transferable Visual Models From Natural Language Supervision
Introducing CLIP, a neural network trained on a massive dataset of image-text pairs that learns to connect images with their textual descriptions, enabling zero-shot image classification and other powerful capabilities.
2022
Making Deep Learning Go Brrrr From First Principles
An in-depth exploration of deep learning system performance optimization, focusing on identifying and addressing bottlenecks.
2017
Attention Is All You Need
A deep dive into the revolutionary Transformer architecture paper that changed the landscape of deep learning.
2021
Data Movement Is All You Need: A Case Study on Optimizing Transformers
A case study on optimizing transformers by focusing on data movement
2015
Deep Residual Learning for Image Recognition
Analysis of the groundbreaking ResNet architecture that enabled training of ultra-deep neural networks.