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Mind the Pad--CNNs Can Develop Blind Spots
B Alsallakh, N Kokhlikyan, V Miglani, J Yuan… - arxiv preprint arxiv …, 2020 - arxiv.org
We show how feature maps in convolutional networks are susceptible to spatial bias. Due to
a combination of architectural choices, the activation at certain locations is systematically …
a combination of architectural choices, the activation at certain locations is systematically …
Svgformer: Representation learning for continuous vector graphics using transformers
D Cao, Z Wang, J Echevarria… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Advances in representation learning have led to great success in understanding and
generating data in various domains. However, in modeling vector graphics data, the pure …
generating data in various domains. However, in modeling vector graphics data, the pure …
A survey of long‐tail item recommendation methods
J Qin - Wireless Communications and Mobile Computing, 2021 - Wiley Online Library
Recommender systems represent a critical field of AI technology applications. The core
function of a recommender system is to recommend items of interest to users, but if it is only …
function of a recommender system is to recommend items of interest to users, but if it is only …
Fingerprint feature extraction by combining texture, minutiae, and frequency spectrum using multi-task CNN
Although most fingerprint matching methods utilize minutia points and/or texture of
fingerprint images as fingerprint features, the frequency spectrum is also a useful feature …
fingerprint images as fingerprint features, the frequency spectrum is also a useful feature …
[HTML][HTML] Automatic diabetic retinopathy grading via self-knowledge distillation
L Luo, D Xue, X Feng - Electronics, 2020 - mdpi.com
Diabetic retinopathy (DR) is a common fundus disease that leads to irreversible blindness,
which plagues the working-age population. Automatic medical imaging diagnosis provides a …
which plagues the working-age population. Automatic medical imaging diagnosis provides a …
Hdsuper: High-quality and high computational utilization edge super-resolution accelerator with hardware-algorithm co-design techniques
Super-resolution (SR) techniques have been employed to construct high-definition images
from low-quality images. Various neural networks have demonstrated excellent image …
from low-quality images. Various neural networks have demonstrated excellent image …
MiniExpNet: A small and effective facial expression recognition network based on facial local regions
Abstract Deep networks based Facial Expression Recognition (FER) have shown excellent
performance. Due to high computational cost and memory resources, it is hard to develop …
performance. Due to high computational cost and memory resources, it is hard to develop …
Early neoplasia identification in Barrett's esophagus via attentive hierarchical aggregation and self-distillation
Automatic surveillance of early neoplasia in Barrett's esophagus (BE) is of great significance
for improving the survival rate of esophageal cancer. It remains, however, a challenging task …
for improving the survival rate of esophageal cancer. It remains, however, a challenging task …
Learning multi-level representations for affective image recognition
Images can convey intense affective experiences and affect people on an affective level.
With the prevalence of online pictures and videos, evaluating emotions from visual content …
With the prevalence of online pictures and videos, evaluating emotions from visual content …
Noah: Learning pairwise object category attentions for image classification
A modern deep neural network (DNN) for image classification tasks typically consists of two
parts: a backbone for feature extraction, and a head for feature encoding and class …
parts: a backbone for feature extraction, and a head for feature encoding and class …