HQTrack is a powerful and practical framework designed to solve a persistent challenge in computer vision: accurately tracking and segmenting…
Instance Segmentation
GCNet: Boost Vision Models with Lightweight Global Context for Better Accuracy and Efficiency 1217
If you’ve worked on computer vision tasks like object detection or instance segmentation, you’ve likely encountered the challenge of modeling…
FastSAM: Real-Time Image Segmentation at 50x Speed Without Sacrificing Accuracy 8193
In today’s fast-paced computer vision landscape, high-quality image segmentation is no longer a luxury—it’s a necessity. Yet, despite the groundbreaking…
OMG-Seg: One Unified Model for All Segmentation Tasks—No More Fragmented Pipelines 1338
For years, computer vision practitioners have juggled a patchwork of specialized models to tackle different segmentation tasks—semantic, instance, panoptic, video,…
YOLOv9: Train-from-Scratch Object Detection That Beats Pretrained Models with Programmable Gradient Information 9391
YOLOv9 marks a significant leap forward in real-time object detection by directly confronting a long-standing but often overlooked problem in…
detrex: A Unified, Modular Benchmark for Detection Transformers—Accelerate Object Detection, Segmentation, and Pose Estimation Research 2250
If you’re evaluating object detection frameworks for a new computer vision project, you’ve likely encountered the rise of DETR (Detection…
RepViT: Real-Time Mobile Vision with Pure CNN Speed and ViT-Level Accuracy 1009
In the world of on-device computer vision, the tension between speed and accuracy has long defined what’s possible. Engineers building…
CARAFE: Boost Dense Prediction Accuracy with Content-Aware, Lightweight Feature Upsampling 32164
Feature upsampling is a critical but often overlooked component in modern computer vision pipelines. Whether you’re building an object detector,…