Explore machine learning papers and reviews related to Computer Vision. Find insights, analysis, and implementation details.
Yunge Li, Lanyu Xu
Reordering image tokens along a Hilbert curve makes 2D local attention block-sparse, for about 4x faster window attention and 18x faster slide attention.
Leyla Naz Candogan, Arshia Afzal, +2
VIOLIN multiplies ViT attention by decay masks from eight space-filling curves: 1,296 extra parameters on DeiT-B and up to 8.7 points on VTAB-1K spatial tasks.
Alex Krizhevsky, Ilya Sutskever, +1
How AlexNet won ImageNet 2012 by a landslide and launched the deep-learning era: a deep convolutional network trained on two GPUs with ReLU activations, dropout regularization, and aggressive data augmentation.
Bin Xiao, Haiping Wu, +7
How Florence-2 turns detection, segmentation, grounding, captioning, and OCR into one sequence-to-sequence problem driven by task prompts, expressing spatial outputs as location tokens in the text vocabulary.
Nikhila Ravi, Valentin Gabeur, +16
How SAM 2 extends promptable segmentation from images to video with a streaming memory bank and memory attention, propagating masks across frames and recovering objects after occlusion.
Haotian Liu, Chunyuan Li, +2
LLaVA paper: align LLMs with visual information through instruction tuning on image-text pairs, enabling multimodal understanding and reasoning.
Yanghao Li, Hanzi Mao, +2
Investigating the effectiveness of plain Vision Transformers as backbones for object detection and proposing modifications to improve their performance.
Joseph Redmon, Santosh Divvala, +2
Introducing YOLO, a unified, real-time object detection system that frames object detection as a single regression problem.
Mingxing Tan, Quoc V. Le
EfficientNet achieves state-of-the-art image classification accuracy with improved efficiency through a novel compound scaling method for CNNs.
Shaoqing Ren, Kaiming He, +2
Faster R-CNN explained: how Region Proposal Networks (RPN) enable near real-time object detection with shared convolutional features.
Alexander Kirillov, Eric Mintun, +3
SAM is a promptable segmentation model that can segment any object in an image using points, boxes, or text prompts with zero-shot generalization.
Junnan Li, Dongxu Li, +2
BLIP-2 leverages frozen image encoders and LLMs for efficient vision-language pre-training, achieving state-of-the-art multimodal performance.
Nicolas Carion, Francisco Massa, +4
Introducing DETR, a novel end-to-end object detection framework that leverages Transformers to directly predict a set of object bounding boxes.
Alexey Dosovitskiy, Lucas Beyer, +10
Vision Transformer (ViT) explained: how splitting images into 16x16 patches enables pure transformer architecture for state-of-the-art image recognition.
Herbert Bay, Tinne Tuytelaars, +1
SURF is a fast and robust algorithm for local feature detection and description, used in object recognition, image registration, and 3D reconstruction.
Ze Liu, Yutong Lin, +6
Swin Transformer: hierarchical Vision Transformer using shifted windows for efficient image classification, object detection, and segmentation.
Alec Radford, Jong Wook Kim, +10
CLIP explained: contrastive learning on 400M image-text pairs enables zero-shot image classification and powerful vision-language understanding.
Kaiming He, Xiangyu Zhang, +2
ResNet analysis: how skip connections and residual learning solved the degradation problem, enabling training of 100+ layer neural networks.