In the rapidly evolving landscape of computer vision, model efficiency and scalability are no longer optional—they’re essential. Enter VMamba, a…
Semantic Segmentation
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,…
MambaVision: Achieve SOTA Image Classification & Downstream Vision Tasks with Hybrid Mamba-Transformer Efficiency 1946
If you’re building computer vision systems that demand both high accuracy and real-world efficiency—without getting bogged down in architectural complexity—MambaVision…
FlexiViT: One Vision Transformer for All Patch Sizes—Deploy Faster or More Accurate Models Without Retraining 3276
Vision Transformers (ViTs) have become a cornerstone of modern computer vision, offering strong performance across a wide range of tasks.…
FastViT: Achieve State-of-the-Art Speed and Accuracy for Vision Tasks on Mobile and Edge Devices 1974
FastViT is a high-performance hybrid vision transformer designed to deliver exceptional speed and accuracy—especially on resource-constrained platforms like mobile phones…
AM-RADIO: Unify Vision Foundation Models into One High-Performance Backbone for Multimodal, Segmentation, and Detection Tasks 1357
In modern computer vision, practitioners often juggle multiple foundation models—CLIP for vision-language alignment, DINOv2 for dense feature extraction, and SAM…
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,…
UNetFormer: Real-Time, High-Accuracy Semantic Segmentation for Urban Remote Sensing Imagery 1007
Semantic segmentation of urban remote sensing imagery—such as aerial photos from drones or satellites—is essential for applications like land cover…