2023
Visual Instruction Tuning
LLaVA paper: align LLMs with visual information through instruction tuning on image-text pairs, enabling multimodal understanding and reasoning.
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
Faster R-CNN explained: how Region Proposal Networks (RPN) enable near real-time object detection with shared convolutional features.
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
Vision Transformer (ViT) explained: how splitting images into 16x16 patches enables pure transformer architecture for state-of-the-art image recognition.
2021
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Swin Transformer: hierarchical Vision Transformer using shifted windows for efficient image classification, object detection, and segmentation.
2021
Learning Transferable Visual Models From Natural Language Supervision
CLIP explained: contrastive learning on 400M image-text pairs enables zero-shot image classification and powerful vision-language understanding.
2022
Making Deep Learning Go Brrrr From First Principles
Deep learning performance optimization from first principles. Learn to identify compute-bound, memory-bound, and overhead bottlenecks with fusion techniques.
2017
Attention Is All You Need
Deep dive into the Transformer architecture that revolutionized NLP. Understand self-attention, multi-head attention, and positional encoding.
2021
Data Movement Is All You Need: A Case Study on Optimizing Transformers
Analysis of transformer performance bottlenecks caused by data movement. Learn optimization strategies for memory-bound operations on GPUs.
2015
Deep Residual Learning for Image Recognition
ResNet analysis: how skip connections and residual learning solved the degradation problem, enabling training of 100+ layer neural networks.