2022-10-12 更新
ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning
Authors:Nicholas Meegan, Hansi Liu, Bryan Cao, Abrar Alali, Kristin Dana, Marco Gruteser, Shubham Jain, Ashwin Ashok
We introduce ViFiCon, a self-supervised contrastive learning scheme which uses synchronized information across vision and wireless modalities to perform cross-modal association. Specifically, the system uses pedestrian data collected from RGB-D camera footage as well as WiFi Fine Time Measurements (FTM) from a user’s smartphone device. We represent the temporal sequence by stacking multi-person depth data spatially within a banded image. Depth data from RGB-D (vision domain) is inherently linked with an observable pedestrian, but FTM data (wireless domain) is associated only to a smartphone on the network. To formulate the cross-modal association problem as self-supervised, the network learns a scene-wide synchronization of the two modalities as a pretext task, and then uses that learned representation for the downstream task of associating individual bounding boxes to specific smartphones, i.e. associating vision and wireless information. We use a pre-trained region proposal model on the camera footage and then feed the extrapolated bounding box information into a dual-branch convolutional neural network along with the FTM data. We show that compared to fully supervised SoTA models, ViFiCon achieves high performance vision-to-wireless association, finding which bounding box corresponds to which smartphone device, without hand-labeled association examples for training data.
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Exploring Localization for Self-supervised Fine-grained Contrastive Learning
Authors:Di Wu, Siyuan Li, Zelin Zang, Stan Z. Li
Self-supervised contrastive learning has demonstrated great potential in learning visual representations. Despite their success in various downstream tasks such as image classification and object detection, self-supervised pre-training for fine-grained scenarios is not fully explored. We point out that current contrastive methods are prone to memorizing background/foreground texture and therefore have a limitation in localizing the foreground object. Analysis suggests that learning to extract discriminative texture information and localization are equally crucial for fine-grained self-supervised pre-training. Based on our findings, we introduce cross-view saliency alignment (CVSA), a contrastive learning framework that first crops and swaps saliency regions of images as a novel view generation and then guides the model to localize on foreground objects via a cross-view alignment loss. Extensive experiments on both small- and large-scale fine-grained classification benchmarks show that CVSA significantly improves the learned representation.
PDF BMVC 2022 camera-ready. 15 pages (main) with 5 pages appendix
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