SegLoc: Visual Self-supervised Learning Scheme for Dense Prediction
Tasks of Security Inspection X-ray Images
- URL: http://arxiv.org/abs/2310.08421v3
- Date: Sat, 21 Oct 2023 10:55:31 GMT
- Title: SegLoc: Visual Self-supervised Learning Scheme for Dense Prediction
Tasks of Security Inspection X-ray Images
- Authors: Shervin Halat, Mohammad Rahmati, Ehsan Nazerfard
- Abstract summary: Self-supervised learning in computer vision has not been able to stay on track comparatively.
In this paper, we evaluate dense prediction tasks on security inspection x-ray images.
Our model has managed to address one of the most challenging downsides of contrastive learning, i.e., false negative pairs of query embeddings.
- Score: 4.251030047034566
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Lately, remarkable advancements of artificial intelligence have been
attributed to the integration of self-supervised learning (SSL) scheme. Despite
impressive achievements within natural language processing (NLP), SSL in
computer vision has not been able to stay on track comparatively. Recently,
integration of contrastive learning on top of existing visual SSL models has
established considerable progress, thereby being able to outperform supervised
counterparts. Nevertheless, the improvements were mostly limited to
classification tasks; moreover, few studies have evaluated visual SSL models in
real-world scenarios, while the majority considered datasets containing
class-wise portrait images, notably ImageNet. Thus, here, we have considered
dense prediction tasks on security inspection x-ray images to evaluate our
proposed model Segmentation Localization (SegLoc). Based upon the model
Instance Localization (InsLoc), our model has managed to address one of the
most challenging downsides of contrastive learning, i.e., false negative pairs
of query embeddings. To do so, our pre-training dataset is synthesized by
cutting, transforming, then pasting labeled segments, as foregrounds, from an
already existing labeled dataset (PIDray) onto instances, as backgrounds, of an
unlabeled dataset (SIXray;) further, we fully harness the labels through
integration of the notion, one queue per class, into MoCo-v2 memory bank,
avoiding false negative pairs. Regarding the task in question, our approach has
outperformed random initialization method by 3% to 6%, while having
underperformed supervised initialization, in AR and AP metrics at different IoU
values for 20 to 30 pre-training epochs.
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