Hands-on Evaluation of Visual Transformers for Object Recognition and Detection

arXiv — cs.CV•Thursday, December 11, 2025 at 5:00:00 AM

PositiveArtificial Intelligence

A recent study evaluated various types of Vision Transformers (ViTs) for object recognition and detection, revealing that hybrid and hierarchical models, particularly Swin and CvT, outperform traditional Convolutional Neural Networks (CNNs) in accuracy and efficiency across tasks like medical image classification and standard datasets such as ImageNet and COCO.
This development is significant as it highlights the potential of ViTs to address limitations faced by CNNs in understanding global image contexts, thereby enhancing performance in critical applications like medical imaging.
The findings contribute to ongoing discussions in the field of artificial intelligence regarding the evolution of visual recognition technologies, emphasizing the need for adaptive methods that can dynamically adjust to image complexity and improve model generalization, as seen in various innovative approaches like LookWhere and Grc-ViT.

— via World Pulse Now AI Editorial System

Read Original

Was this article worth reading? Share it

One More Thing in AI

Master AI with curated tools and tutorials for practical, real-world applications.

WasItAI

Verify if your images are AI-generated with this simple detection tool.

Business & ProductivityView app details

The Visualizer

Transform complex topics into clear, visual explanations for effortless learning.

AI & DataView app details

Tattoo Visualizer

Generate and explore AI-designed tattoos from a vast visual library.

AI & DataView app details

Attentive AI

Extract digital maps from satellite, aerial, and drone imagery using deep learning.

AI & DataView app details

HomeVisualizer.AI

AI transforms your ideas into realistic home visualizations instantly.

AI & DataView app details

Continue Readings

arXiv — cs.CL3 days ago

Efficiently Seeking Flat Minima for Better Generalization in Fine-Tuning Large Language Models and Beyond

PositiveArtificial Intelligence

Recent research has introduced Flat Minima LoRA (FMLoRA) and its efficient variant EFMLoRA, aimed at enhancing the generalization of large language models by seeking flat minima in low-rank adaptation (LoRA). This approach theoretically demonstrates that perturbations in the full parameter space can be effectively transferred to the low-rank subspace, minimizing interference from multiple matrices.

Read full article

via arXiv — cs.CL

arXiv — cs.CV3 days ago

Do We Need Reformer for Vision? An Experimental Comparison with Vision Transformers

NeutralArtificial Intelligence

Recent research has explored the Reformer architecture as a potential alternative to Vision Transformers (ViTs) in computer vision, addressing the computational inefficiencies of standard ViTs that utilize global self-attention. The study demonstrates that the Reformer can reduce time complexity from O(n^2) to O(n log n) while maintaining performance on datasets like CIFAR-10 and ImageNet-100.

Read full article

via arXiv — cs.CV

Ready to build your own newsroom?

Subscribe to unlock a personalised feed, podcasts, newsletters, and notifications tailored to the topics you actually care about