86 citations · 294 across the 26 of their papers we have counts for
26 papers
Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets
Xiangyu Chen, Qinghao Hu, Kaidong Li +2
Vision Transformers has demonstrated competitive performance on computer vision tasks benefiting from their ability to capture long-range dependencies with multi-head self-attentio…
Aggregating Global Features into Local Vision Transformer
Krushi Patel, Andres M. Bur, Fengjun Li +1
Local Transformer-based classification models have recently achieved promising results with relatively low computational costs. However, the effect of aggregating spatial global in…
Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs
Tianxiao Zhang, Bo Luo, Ajay Sharda +1
Label assignment plays a significant role in modern object detection models. Detection models may yield totally different performances with different label assignment strategies. F…
An Unsupervised Domain Adaptation Model based on Dual-module Adversarial Training
Yiju Yang, Tianxiao Zhang, Guanyu Li +2
In this paper, we propose a dual-module network architecture that employs a domain discriminative feature module to encourage the domain invariant feature module to learn more doma…
Miti-DETR: Object Detection based on Transformers with Mitigatory Self-Attention Convergence
Wenchi Ma, Tianxiao Zhang, Guanghui Wang
Object Detection with Transformers (DETR) and related works reach or even surpass the highly-optimized Faster-RCNN baseline with self-attention network architectures. Inspired by t…
Semantic Clustering based Deduction Learning for Image Recognition and Classification
Wenchi Ma, Xuemin Tu, Bo Luo +1
The paper proposes a semantic clustering based deduction learning by mimicking the learning and thinking process of human brains. Human beings can make judgments based on experienc…