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In the ever-evolving landscape of artificial intelligence, we have witnessed the launch of groundbreaking vision models that pushed the boundaries of computer vision. These cutting-edge models harnessed advanced neural network architectures, sophisticated training techniques, and unprecedented data sets to redefine the capabilities of visual perception — from enhanced object recognition to nuanced scene understanding.
Here is a list of the top 7 vision models launched this year.
DINOv2
Meta AI has developed DINOv2, an innovative method for training high-performance computer vision models that delivers exceptional performance and does not require fine-tuning. As a result, it is well-suited to serve as a backbone for various computer vision tasks.
Thanks to its self-supervised learning approach, DINOv2 can learn from any image collection and acquire features that the current standard approach cannot, such as depth estimation.
In 2023, DINOv2 was open-sourced, becoming the first method for training computer vision models to use self-supervised learning to achieve results that match or surpass the standard approach used in the field. Self-supervised learning is a potent and adaptable way to train AI models since it does not require vast amounts of labelled data.
The models can be trained on any image collection, regardless of whether they have associated metadata, and learn from all images they are given. This approach expands the potential applications of computer vision models, making them more versatile and powerful than ever before.
2. YOLOv8
The YOLO (You Only Look Once) series of models are very well-known in the computer vision world. YOLO is famous because it achieves high accuracy while having a small model size. It can be trained on a single GPU. Machine learning practitioners can deploy it on edge hardware or in the cloud at a low cost.
YOLOv8 is the latest YOLO model that uses advanced technology to detect objects, classify images, and segment instances. It was created by Ultralytics, the same team that developed the highly influential YOLOv5 model. YOLOv8 brings various architectural and developmental improvements over its predecessor YOLOv5.
As of now, Ultralytics is actively developing YOLOv8 and working on new features while considering feedback from the community. The organisation ensures that their models receive long-term support and collaborates with the community to improve the model’s performance.
3. EfficientViT
Vision transformers are a widely used framework in computer vision, as they provide high computational capabilities and superior performance. However, while these models continue to improve in accuracy and performance, they also come with higher operational costs and computational overhead. The EfficientNet model was developed to address this issue.
It seeks to enhance the performance of vision transformers and determine the principles for designing efficient and effective transformer-based framework architectures. The EfficientViT model is built on existing vision transformer frameworks such as Swim and DeiT. It analyses three critical factors that affect the speed of model interference, including computation redundancy, memory access, and parameter usage.
4. SWIN Transformer
The field of medical image segmentation can be challenging due to the need for large amounts of pre-training data, which is difficult to acquire. However, recent advancements in large-scale Vision Transformers have led to significant progress in improving pre-trained models for this purpose.
One such advancement is the Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline that enables accurate and data-efficient self-supervised medical image analysis. SwinMM utilises a masked multi-view encoder during the pre-training phase to train masked multi-view observations through image reconstruction, rotation, contrastive learning, and a novel task that capitalises on the consistency between predictions from various perspectives.
During the fine-tuning stage, a cross-view decoder aggregates the multi-view information through a cross-attention block. SwinMM outperforms the previous state-of-the-art self-supervised learning method. SwinMM shows great potential for future applications in medical imaging.
5. SimCLR
The SimCLR vision model is designed to learn image representations from unlabeled data by generating positive and negative image samples using image augmentation. It then minimises the contrastive loss function to explore more underlying structural information. SimCLR-Inception model, the new version launched in 2023, achieves better results and the accuracy is at least 4% higher than the other compared models such as LeNet, VGG16, Inception V3, and EfficientNet V2, indicating that this model could work better for robot vision.
SimCLR maps the previous conceptual components onto a deep neural network architecture, inspired by residual neural networks (ResNet). Initially, SimCLR randomly selects examples from the original dataset, transforming each example twice using a combination of simple augmentations (random cropping, random colour distortion, and Gaussian blur), creating two sets of corresponding views.
6. StyleGAN3 by NVIDIA
The progress in generating artificial images has been impressive, thanks to the StyleGAN architecture, which is skilled in creating realistic facial images. In 2023, a group of researchers from NVIDIA and Aalto University introduced StyleGAN 3, which addressed a significant weakness in current generative models. This breakthrough has created numerous possibilities for using these models in video and animation.
Developing this new model was made easier by the well-documented and organised code base and the high level of compatibility with previous versions. It didn’t take long before people began to guide the outputs of StyleGAN3 with CLIP, resulting in beautiful outcomes. In StyleGAN3, each feature’s precise sub-pixel location is solely passed down from the underlying coarse features, resulting in a more natural transformation hierarchy.
7. MUnit
MUnit is a testing framework for Mule applications that enables you to create automated tests for your APIs and integrations with ease. It offers a comprehensive set of integration and unit testing capabilities and comes fully integrated with Maven and Surefire, making it ideal for use in continuous deployment environments.
In MUnit3, it is assumed that images can be broken down into a content code that is invariant across domains, and a style code that captures domain-specific properties. To translate an image to another domain, the content code is combined with a random style code sampled from the target domain’s style space.
Finally, the latest version makes it possible for users to control the style of translation outputs by providing an example style image.
