49 research outputs found
Image Labels Are All You Need for Coarse Seagrass Segmentation
Seagrass meadows serve as critical carbon sinks, but estimating the amount of
carbon they store requires knowledge of the seagrass species present.
Underwater and surface vehicles equipped with machine learning algorithms can
help to accurately estimate the composition and extent of seagrass meadows at
scale. However, previous approaches for seagrass detection and classification
have required supervision from patch-level labels. In this paper, we reframe
seagrass classification as a weakly supervised coarse segmentation problem
where image-level labels are used during training (25 times fewer labels
compared to patch-level labeling) and patch-level outputs are obtained at
inference time. To this end, we introduce SeaFeats, an architecture that uses
unsupervised contrastive pre-training and feature similarity, and SeaCLIP, a
model that showcases the effectiveness of large language models as a
supervisory signal in domain-specific applications. We demonstrate that an
ensemble of SeaFeats and SeaCLIP leads to highly robust performance. Our method
outperforms previous approaches that require patch-level labels on the
multi-species 'DeepSeagrass' dataset by 6.8% (absolute) for the class-weighted
F1 score, and by 12.1% (absolute) for the seagrass presence/absence F1 score on
the 'Global Wetlands' dataset. We also present two case studies for real-world
deployment: outlier detection on the Global Wetlands dataset, and application
of our method on imagery collected by the FloatyBoat autonomous surface
vehicle.Comment: 10 pages, 4 figures, additional 3 pages of supplementary materia
A Novel Framework for Online Amnesic Trajectory Compression in Resource-constrained Environments
State-of-the-art trajectory compression methods usually involve high
space-time complexity or yield unsatisfactory compression rates, leading to
rapid exhaustion of memory, computation, storage and energy resources. Their
ability is commonly limited when operating in a resource-constrained
environment especially when the data volume (even when compressed) far exceeds
the storage limit. Hence we propose a novel online framework for error-bounded
trajectory compression and ageing called the Amnesic Bounded Quadrant System
(ABQS), whose core is the Bounded Quadrant System (BQS) algorithm family that
includes a normal version (BQS), Fast version (FBQS), and a Progressive version
(PBQS). ABQS intelligently manages a given storage and compresses the
trajectories with different error tolerances subject to their ages. In the
experiments, we conduct comprehensive evaluations for the BQS algorithm family
and the ABQS framework. Using empirical GPS traces from flying foxes and cars,
and synthetic data from simulation, we demonstrate the effectiveness of the
standalone BQS algorithms in significantly reducing the time and space
complexity of trajectory compression, while greatly improving the compression
rates of the state-of-the-art algorithms (up to 45%). We also show that the
operational time of the target resource-constrained hardware platform can be
prolonged by up to 41%. We then verify that with ABQS, given data volumes that
are far greater than storage space, ABQS is able to achieve 15 to 400 times
smaller errors than the baselines. We also show that the algorithm is robust to
extreme trajectory shapes.Comment: arXiv admin note: substantial text overlap with arXiv:1412.032
Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection
Current video object detection (VOD) models often encounter issues with
over-aggregation due to redundant aggregation strategies, which perform feature
aggregation on every frame. This results in suboptimal performance and
increased computational complexity. In this work, we propose an image-level
Object Detection Difficulty (ODD) metric to quantify the difficulty of
detecting objects in a given image. The derived ODD scores can be used in the
VOD process to mitigate over-aggregation. Specifically, we train an ODD
predictor as an auxiliary head of a still-image object detector to compute the
ODD score for each image based on the discrepancies between detection results
and ground-truth bounding boxes. The ODD score enhances the VOD system in two
ways: 1) it enables the VOD system to select superior global reference frames,
thereby improving overall accuracy; and 2) it serves as an indicator in the
newly designed ODD Scheduler to eliminate the aggregation of frames that are
easy to detect, thus accelerating the VOD process. Comprehensive experiments
demonstrate that, when utilized for selecting global reference frames, ODD-VOD
consistently enhances the accuracy of Global-frame-based VOD models. When
employed for acceleration, ODD-VOD consistently improves the frames per second
(FPS) by an average of 73.3% across 8 different VOD models without sacrificing
accuracy. When combined, ODD-VOD attains state-of-the-art performance when
competing with many VOD methods in both accuracy and speed. Our work represents
a significant advancement towards making VOD more practical for real-world
applications.Comment: 11 pages, 6 figures, accepted by ACM MM202
Towards Optimal Kinetic Energy Harvesting for the Batteryless IoT
Traditional Internet of Things (IoT) sensors rely on batteries that need to
be replaced or recharged frequently which impedes their pervasive deployment. A
promising alternative is to employ energy harvesters that convert the
environmental energy into electrical energy. Kinetic Energy Harvesting (KEH)
converts the ambient motion/vibration energy into electrical energy to power
the IoT sensor nodes. However, most previous works employ KEH without
dynamically tracking the optimal operating point of the transducer for maximum
power output. In this paper, we systematically analyse the relation between the
operating point of the transducer and the corresponding energy yield. To this
end, we explore the voltage-current characteristics of the KEH transducer to
find its Maximum Power Point (MPP). We show how this operating point can be
approximated in a practical energy harvesting circuit. We design two hardware
circuit prototypes to evaluate the performance of the proposed mechanism and
analyse the harvested energy using a precise load shaker under a wide set of
controlled conditions typically found in human-centric applications. We analyse
the dynamic current-voltage characteristics and specify the relation between
the MPP sampling rate and harvesting efficiency which outlines the need for
dynamic MPP tracking. The results show that the proposed energy harvesting
mechanism outperforms the conventional method in terms of generated power and
offers at least one order of magnitude higher power than the latter
Understanding the Effects of Projectors in Knowledge Distillation
Conventionally, during the knowledge distillation process (e.g. feature
distillation), an additional projector is often required to perform feature
transformation due to the dimension mismatch between the teacher and the
student networks. Interestingly, we discovered that even if the student and the
teacher have the same feature dimensions, adding a projector still helps to
improve the distillation performance. In addition, projectors even improve
logit distillation if we add them to the architecture too. Inspired by these
surprising findings and the general lack of understanding of the projectors in
the knowledge distillation process from existing literature, this paper
investigates the implicit role that projectors play but so far have been
overlooked. Our empirical study shows that the student with a projector (1)
obtains a better trade-off between the training accuracy and the testing
accuracy compared to the student without a projector when it has the same
feature dimensions as the teacher, (2) better preserves its similarity to the
teacher beyond shallow and numeric resemblance, from the view of Centered
Kernel Alignment (CKA), and (3) avoids being over-confident as the teacher does
at the testing phase. Motivated by the positive effects of projectors, we
propose a projector ensemble-based feature distillation method to further
improve distillation performance. Despite the simplicity of the proposed
strategy, empirical results from the evaluation of classification tasks on
benchmark datasets demonstrate the superior classification performance of our
method on a broad range of teacher-student pairs and verify from the aspects of
CKA and model calibration that the student's features are of improved quality
with the projector ensemble design.Comment: arXiv admin note: text overlap with arXiv:2210.1527
Long-Term Energy-Neutral Operation of Solar Energy-Harvesting Sensor Nodes under Time-Varying Utility [Poster Abstract]
Sensor networks increasingly rely on harvesting energy from the environment to sense, process, and transmit data. Online energy availability forecasting and energy management are critical to ensure long-term energy-neutral operation of battery-powered energy-harvesting sensor nodes. Existing methods focus on applications with time-invariant utility and custom-tailored hardware platforms, which limits their effectiveness across diverse application domains, different platforms, and in the face of aging hardware components. To address these limitations, we formulate an optimisation problem with respect to time-varying utility under the given hardware constraints. We also present PREACT, an online energy-management algorithm that approximates the optimal solution to the optimisation problem by incorporating long-term energy forecasting