Visual Geo-localization with Self-supervised Representation Learning

Visual Geo-localization (VG) has emerged as a significant research area, aiming to identify geolocation based on visual features. Most VG approaches use learnable feature extractors for representation learning. Recently, Self-Supervised Learning (SSL) methods have also demonstrated comparable performance to supervised methods by using numerous unlabeled images for representation learning. In this work, we present a novel unified VG-SSL framework with the goal to enhance performance and training efficiency on a large VG dataset by SSL methods. Our work incorporates multiple SSL methods tailored for VG: SimCLR, MoCov2, BYOL, SimSiam, Barlow Twins, and VICReg. We systematically analyze the performance of different training strategies and study the optimal parameter settings for the adaptation of SSL methods for the VG task. The results demonstrate that our method, without the significant computation and memory usage associated with Hard Negative Mining (HNM), can match or even surpass the VG performance of the baseline that employs HNM. The code is available at https://github.com/arplaboratory/VG_SSL.

Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals

Most tracking-by-detection methods employ a local search window around the predicted object location in the current frame assuming the previous location is accurate, the trajectory is smooth, and the computational capacity permits a search radius that can accommodate the maximum speed yet small enough to reduce mismatches. These, however, may not be valid always, in particular for fast and irregularly moving objects. Here, we present an object tracker that is not limited to a local search window and has ability to probe efficiently the entire frame. Our method generates a small number of "high-quality" proposals by a novel instance-specific objectness measure and evaluates them against the object model that can be adopted from an existing tracking-by-detection approach as a core tracker. During the tracking process, we update the object model concentrating on hard false-positives supplied by the proposals, which help suppressing distractors caused by difficult background clutters, and learn how to re-rank proposals according to the object model. Since we reduce significantly the number of hypotheses the core tracker evaluates, we can use richer object descriptors and stronger detector. Our method outperforms most recent state-of-the-art trackers on popular tracking benchmarks, and provides improved robustness for fast moving objects as well as for ultra low-frame-rate videos.

Tracking Randomly Moving Objects on Edge Box Proposals

Most tracking-by-detection methods employ a local search window around the predicted object location in the current frame assuming the previous location is accurate, the trajectory is smooth, and the computational capacity permits a search radius that can accommodate the maximum speed yet small enough to reduce mismatches. These, however, may not be valid always, in particular for fast and irregularly moving objects. Here, we present an object tracker that is not limited to a local search window and has ability to probe efficiently the entire frame. Our method generates a small number of "high-quality" proposals by a novel instance-specific objectness measure and evaluates them against the object model that can be adopted from an existing tracking-by-detection approach as a core tracker. During the tracking process, we update the object model concentrating on hard false-positives supplied by the proposals, which help suppressing distractors caused by difficult background clutters, and learn how to re-rank proposals according to the object model. Since we reduce significantly the number of hypotheses the core tracker evaluates, we can use richer object descriptors and stronger detector. Our method outperforms most recent state-of-the-art trackers on popular tracking benchmarks, and provides improved robustness for fast moving objects as well as for ultra low-frame-rate videos.