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06/09/2023
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding
Recently, large language models (LLMs) have made significant advancement...
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12/09/2022
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Mix-up training approaches have proven to be effective in improving the ...
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06/04/2022
Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation
Data augmentation has been proven to be an effective technique for devel...
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04/09/2022
The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization
Training with an emphasis on "hard-to-learn" components of the data has ...
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11/29/2021
On the Integration of Self-Attention and Convolution
Convolution and self-attention are two powerful techniques for represent...
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11/05/2021
Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features
Machine learning has demonstrated remarkable prediction accuracy over i....
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05/31/2021
Not All Images are Worth 16x16 Words: Dynamic Vision Transformers with Adaptive Sequence Length
Vision Transformers (ViT) have achieved remarkable success in large-scal...
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11/25/2020
Squared ℓ_2 Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Data augmentation is one of the most popular techniques for improving th...
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10/22/2020
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection
Weakly Supervised Object Detection (WSOD) has emerged as an effective to...
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07/05/2020
Self-Challenging Improves Cross-Domain Generalization
Convolutional Neural Networks (CNN) conduct image classification by acti...
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12/04/2019
Multiple Anchor Learning for Visual Object Detection
Classification and localization are two pillars of visual object detecto...
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03/28/2019
Improving Object Detection with Inverted Attention
Improving object detectors against occlusion, blur and noise is a critic...
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03/15/2017
