[HTML][HTML] Deep learning in optical metrology: a review
With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …
metrology has become versatile problem-solving backbones in manufacturing, fundamental …
Social physics
Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …
phenomena. This development has been due to physicists venturing outside of their …
Tip-adapter: Training-free adaption of clip for few-shot classification
Abstract Contrastive Vision-Language Pre-training, known as CLIP, has provided a new
paradigm for learning visual representations using large-scale image-text pairs. It shows …
paradigm for learning visual representations using large-scale image-text pairs. It shows …
Learning to prompt for open-vocabulary object detection with vision-language model
Recently, vision-language pre-training shows great potential in open-vocabulary object
detection, where detectors trained on base classes are devised for detecting new classes …
detection, where detectors trained on base classes are devised for detecting new classes …
Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference
Few-shot learning (FSL) is an important and topical problem in computer vision that has
motivated extensive research into numerous methods spanning from sophisticated meta …
motivated extensive research into numerous methods spanning from sophisticated meta …
Learning what not to segment: A new perspective on few-shot segmentation
Recently few-shot segmentation (FSS) has been extensively developed. Most previous
works strive to achieve generalization through the meta-learning framework derived from …
works strive to achieve generalization through the meta-learning framework derived from …
Joint distribution matters: Deep brownian distance covariance for few-shot classification
Few-shot classification is a challenging problem as only very few training examples are
given for each new task. One of the effective research lines to address this challenge …
given for each new task. One of the effective research lines to address this challenge …
Forward compatible few-shot class-incremental learning
Novel classes frequently arise in our dynamically changing world, eg, new users in the
authentication system, and a machine learning model should recognize new classes without …
authentication system, and a machine learning model should recognize new classes without …
Learning from few examples: A summary of approaches to few-shot learning
Few-Shot Learning refers to the problem of learning the underlying pattern in the data just
from a few training samples. Requiring a large number of data samples, many deep learning …
from a few training samples. Requiring a large number of data samples, many deep learning …
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions
The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …
artificial intelligence-related technologies. In engineering scenarios, machines usually work …