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Found 7 papers, 3 papers with code
Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions
By prompting LLMs in various ways, we generate descriptions that capture visual appearance, habitat, and geographic regions and pair them with existing attributes such as the taxonomic structure of the categories.
PARTICLE: Part Discovery and Contrastive Learning for Fine-grained Recognition
For example, under a linear-evaluation scheme, the classification accuracy of a ResNet50 trained on ImageNet using DetCon, a self-supervised learning approach, improves from 35. 4% to 42. 0% on the Caltech-UCSD Birds, from 35. 5% to 44. 1% on the FGVC Aircraft, and from 29. 7% to 37. 4% on the Stanford Cars.
Improving Few-Shot Part Segmentation using Coarse Supervision
A significant bottleneck in training deep networks for part segmentation is the cost of obtaining detailed annotations.
GANORCON: Are Generative Models Useful for Few-shot Segmentation?
Motivated by this we present an alternative approach based on contrastive learning and compare their performance on standard few-shot part segmentation benchmarks.
RecSal : Deep Recursive Supervision for Visual Saliency Prediction
This information when utilized in a biologically inspired fashion can contribute in better prediction performance without the use of models with huge number of parameters.
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Standard Convolutional Neural Networks (CNNs) designed for computer vision tasks tend to have large intermediate activation maps.
Ranked #27 on
Face Detection
on WIDER Face (Medium)
Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy
This paper proposes a method for the automated segmentation of retinal lesions and optic disk in fundus images using a deep fully convolutional neural network for semantic segmentation.
