91 research outputs found
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification
In person re-identification (ReID) task, because of its shortage of trainable
dataset, it is common to utilize fine-tuning method using a classification
network pre-trained on a large dataset. However, it is relatively difficult to
sufficiently fine-tune the low-level layers of the network due to the gradient
vanishing problem. In this work, we propose a novel fine-tuning strategy that
allows low-level layers to be sufficiently trained by rolling back the weights
of high-level layers to their initial pre-trained weights. Our strategy
alleviates the problem of gradient vanishing in low-level layers and robustly
trains the low-level layers to fit the ReID dataset, thereby increasing the
performance of ReID tasks. The improved performance of the proposed strategy is
validated via several experiments. Furthermore, without any add-ons such as
pose estimation or segmentation, our strategy exhibits state-of-the-art
performance using only vanilla deep convolutional neural network architecture.Comment: Accepted to AAAI 201
MHSA-Net: Multi-Head Self-Attention Network for Occluded Person Re-Identification
This paper presents a novel person re-identification model, named Multi-Head
Self-Attention Network (MHSA-Net), to prune unimportant information and capture
key local information from person images. MHSA-Net contains two main novel
components: Multi-Head Self-Attention Branch (MHSAB) and Attention Competition
Mechanism (ACM). The MHSAM adaptively captures key local person information,
and then produces effective diversity embeddings of an image for the person
matching. The ACM further helps filter out attention noise and non-key
information. Through extensive ablation studies, we verified that the
Structured Self-Attention Branch and Attention Competition Mechanism both
contribute to the performance improvement of the MHSA-Net. Our MHSA-Net
achieves state-of-the-art performance especially on images with occlusions. We
have released our models (and will release the source codes after the paper is
accepted) on https://github.com/hongchenphd/MHSA-Net.Comment: Submitted to IEEE Transactions on Image Processing (TIP
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