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Found 7 papers, 2 papers with code
Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition
Trained through an end-to-end multi-task learning process, this method enhances performance in the fine-grained food recognition task, showing exceptional prowess with highly similar classes.
Ranked #4 on
Fine-Grained Image Classification
on Food-101
Fine-Grained Image Classification
Fine-Grained Image Recognition
+2
All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy Reduction
Nearest neighbour based methods have proved to be one of the most successful self-supervised learning (SSL) approaches due to their high generalization capabilities.
ELFIS: Expert Learning for Fine-grained Image Recognition Using Subsets
Extensive experimentation shows improvements in the SoTA FGVR benchmarks of up to +1. 3% of accuracy using both CNNs and transformer-based networks.
Fine-Grained Image Recognition
Fine-Grained Visual Recognition
Learning Multi-Subset of Classes for Fine-Grained Food Recognition
We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets.
Ranked #1 on
Fine-Grained Image Classification
on FoodX-251
Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation
Given the hyper-spectral imaging unique potentials in grasping the polymer characteristics of different materials, it is commonly used in sorting procedures.
Deep Net Features for Complex Emotion Recognition
This paper investigates the influence of different acoustic features, audio-events based features and automatic speech translation based lexical features in complex emotion recognition such as curiosity.
Deep Learning as Feature Encoding for Emotion Recognition
Deep learning is popular as an end-to-end framework extracting the prominent features and performing the classification also.
