HetDPT: The Future of Speedy Vision Transformers?
HetDPT introduces a novel way to speed up Vision Transformers by focusing on depth pruning while maintaining accuracy. Is this the breakthrough AI researchers have been waiting for?
Read the full articleYou might also wanna read
ViT^3: Vision Test-Time Training — A CVPR 2026 Best Paper Finalist on Efficient Sequence Modeling
[CVPR 2026] [Best Paper Finalist] [Oral] Official repository of Vision Test-Time Training - LeapLabTHU/ViTTT
ZipDepth: An ultra-lightweight neural network for depth estimation on mobile devices
ZipDepth has been introduced—a compact neural network with 6.1 million parameters for high-speed monocular depth estimation on Edge platform

IBM researchers break up with traditional transformers in new gen AI model architecture
IBM researchers propose a novel architecture for light-weight generative AI models.
Systematic Study Shows Transformers Can Drop One or More QKV Projections Without Quality Loss
Transformers have become the standard solution for various AI tasks, with the query, key, and value (QKV) attention formulation playing a ce
Systematic Study Shows Transformers Can Drop One or More QKV Projections Without Quality Loss
Transformers have become the standard solution for various AI tasks, with the query, key, and value (QKV) attention formulation playing a ce

All-Optical Chip Enables Large-Scale AI Semantic Vision Generation
Large-scale generative artificial intelligence (AI) is facing a severe computing power shortage. Although photonic computing achieves excell
YOLO26: New Real-Time Vision AI Model Family Removes NMS for Lower Latency, Optimizes for Edge Hardware
YOLO26 brings faster CPU inference, small-object accuracy, and edge optimization to the YOLO family. See how it stacks up against today’s le
blog.roboflow.com·18d ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.