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Found 4 papers, 4 papers with code
Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition
The PMAL framework achieves high recognition accuracy by treating image denoising as an additional task in image recognition and progressively forcing a model to learn noise invariance.
Ranked #1 on
Fine-Grained Image Classification
on CompCars
Learn from Each Other to Classify Better: Cross-layer Mutual Attention Learning for Fine-grained Visual Classification
Specifically, this work views the shallow to deep layers of CNNs as “experts” knowledgeable about different perspectives.
Ranked #2 on
Fine-Grained Image Classification
on Stanford Cars
(using extra training data)
Toward Extremely Lightweight Distracted Driver Recognition With Distillation-Based Neural Architecture Search and Knowledge Transfer
Experimental results on the Statefarm Distracted Driver Detection Dataset and AUC Distracted Driver Dataset show that the proposed approach is highly effective for recognizing distracted driving behaviors from photos: (1) the teacher network's accuracy surpasses the previous best accuracy; (2) the student network achieves very high accuracy with only 0. 42M parameters (around 55% of the previous most lightweight model).
Contrastively-reinforced Attention Convolutional Neural Network for Fine-grained Image Recognition
The evaluation information is backpropagated and forces the classification stream to improve its awareness of visual attention, which helps classification.
Ranked #7 on
Fine-Grained Image Classification
on CUB-200-2011
