Deep learning for retail product recognition: Challenges and techniques
Taking time to identify expected products and waiting for the checkout in a retail store are
common scenes we all encounter in our daily lives. The realization of automatic product …
common scenes we all encounter in our daily lives. The realization of automatic product …
Learning attentive pairwise interaction for fine-grained classification
Fine-grained classification is a challenging problem, due to subtle differences among highly-
confused categories. Most approaches address this difficulty by learning discriminative …
confused categories. Most approaches address this difficulty by learning discriminative …
Attention convolutional binary neural tree for fine-grained visual categorization
Fine-grained visual categorization (FGVC) is an important but challenging task due to high
intra-class variances and low inter-class variances caused by deformation, occlusion …
intra-class variances and low inter-class variances caused by deformation, occlusion …
Filtration and distillation: Enhancing region attention for fine-grained visual categorization
Delicate attention of the discriminative regions plays a critical role in Fine-Grained Visual
Categorization (FGVC). Unfortunately, most of the existing attention models perform poorly …
Categorization (FGVC). Unfortunately, most of the existing attention models perform poorly …
Low-rank pairwise alignment bilinear network for few-shot fine-grained image classification
Deep neural networks have demonstrated advanced abilities on various visual classification
tasks, which heavily rely on the large-scale training samples with annotated ground-truth …
tasks, which heavily rely on the large-scale training samples with annotated ground-truth …
Graph-propagation based correlation learning for weakly supervised fine-grained image classification
Abstract The key of Weakly Supervised Fine-grained Image Classification (WFGIC) is how to
pick out the discriminative regions and learn the discriminative features from them. However …
pick out the discriminative regions and learn the discriminative features from them. However …
Weakly supervised fine-grained image classification via guassian mixture model oriented discriminative learning
Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick
out the discriminative regions from the high-level feature maps directly. We discover that due …
out the discriminative regions from the high-level feature maps directly. We discover that due …
Look-into-object: Self-supervised structure modeling for object recognition
Most object recognition approaches predominantly focus on learning discriminative visual
patterns, while overlooking the holistic object structure. Though important, structure …
patterns, while overlooking the holistic object structure. Though important, structure …
Correspondence networks with adaptive neighbourhood consensus
In this paper, we tackle the task of establishing dense visual correspondences between
images containing objects of the same category. This is a challenging task due to large intra …
images containing objects of the same category. This is a challenging task due to large intra …
Multi-path deep cnns for fine-grained car recognition
H Wang, J Peng, Y Zhao, X Fu - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Along with the growing demands of intelligent traffic system, how to recognize the category
information of a car from surveillance cameras has been an important task. Fine-grained car …
information of a car from surveillance cameras has been an important task. Fine-grained car …