Fine-Grained Visual Categorization
26 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Fine-Grained Visual Categorization
MTL-AQA
VegFru
FoodX-251
L-Bird
FeathersV1
Latest papers with no code
R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction
In this paper, we present a novel approach for FGVC, which can simultaneously make use of partial yet sufficient discriminative information in environmental cues and also compress the redundant information in class-token with respect to the target.
Fine-Grained Adversarial Semi-supervised Learning
Our approach leverages unlabeled data with an adversarial optimization strategy in which the internal features representation is obtained with a second-order pooling model.
A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization
Motivated by these issues, this paper proposes a novel compositional feature embedding and similarity metric (CECS).
Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization
The advantage of the proposed method is that the feature detection and extraction model only requires a small amount of target region samples with bounding boxes for training, then it can automatically locate the target area for a large number of images in the dataset at a high detection accuracy.
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization
From this analysis, we both highlight the importance of reporting and comparing methods based on information beyond overall accuracy, as well as point out techniques that mitigate variance in FGVC results.
VeriMedi: Pill Identification using Proxy-based Deep Metric Learning and Exact Solution
After that, the segmented pill images are sent to the identification solution where a Deep Metric Learning model that is trained with Proxy Anchor Loss (PAL) function generates embedding vectors for each pill image.
Alignment Enhancement Network for Fine-grained Visual Categorization
However, they are still inefficient to fully use the cross-layer information based on the simple aggregation strategy, while existing pairwise learning methods also fail to explore long-range interactions between different images.
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization
We perform our experiment on six challenging and commonly used FGVC datasets, and we show competitive improvement on accuracies by using attention-aware data augmentation techniques with features derived from deep learning model InceptionV3, pre-trained on large scale datasets.
Facing the Hard Problems in FGVC
In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy.
FenceMask: A Data Augmentation Approach for Pre-extracted Image Features
It is based on the 'simulation of object occlusion' strategy, which aim to achieve the balance between object occlusion and information retention of the input data.
