Latest Articles for Image Recognition

A study explores new methods for deep learning models to adapt to changing data.

A look into how MLLMs handle small details in images.

New datasets reveal difficulties in identifying racers and text in muddy conditions.

A new method enhances person recognition using weakly labeled data.

Strategies to improve model performance despite uneven data distribution.

This study uncovers what attackers know in adversarial attacks against image recognition models.

Examining limitations of large vision-language models in detailed image understanding.

Examining self-attention and gradient descent in transformer models.

A new method enhances model focus on important features during image classification.

Innovative method simplifies training models for complex image classification.

A new method enhances the testing of visual entailment systems by focusing on object relationships.

A new framework improves image recognition accuracy and efficiency for web entities.

Addressing challenges in federated learning due to diverse devices and data.

A new approach improves recognition for underrepresented classes in computer vision.

A new method improves category discovery by combining visual and text information.

A new method enhances the accuracy of names in image segmentation datasets.

This article examines how adversarial attacks alter the learned concepts of CNNs.

A new method improves image-text alignment by reducing single tag bias.

This article explores how training data affects model performance in multimodal systems.

A new lightweight model improves target recognition in synthetic aperture radar images.

A new method improves labeling of unlabelled data in image recognition.

MCPNet offers clearer insights into deep learning model decisions through multi-level concept extraction.

New method improves image classification with minimal data using unlabeled images.

A study highlights CLIP's reliance on spurious features in image recognition.

A method to adapt machine learning models without needing source data.

Introducing FiLo: A novel method for accurate anomaly detection and localization.

A new method enhances object classification accuracy using multiple representations.

A new training method enhances neural networks' ability to assess uncertainty.

Discover how CPEA method enhances image classification with minimal data.

This study investigates biases in vision-language models and ways to reduce their impact.

Capsule Networks improve object recognition with unique structures and learning methods.

HDC framework improves object recognition using language descriptions in images.

A new method enhances image perception in language models using diffusion models.

VLMs struggle with image classification, but better data integration can enhance their capabilities.

New framework improves image recognition across different domains using language descriptions.

Introducing PART, a method to boost machine learning models' accuracy and robustness.

New methods enhance image recognition for identifying people across different environments.

A new approach enhances how AI compares images using visual instructions.

FALCON identifies fine categories using broad labels without needing detailed supervision.

A look into neural networks, uncertainty, and their impact on AI decision-making.