Search Results for author:
Found 15 papers, 6 papers with code
Human-in-the-Loop Segmentation of Multi-species Coral Imagery
For extremely sparsely labeled images, we propose a labeling regime based on human-in-the-loop principles, resulting in significant improvement in annotation efficiency: If only 5 point labels per image are available, our proposed human-in-the-loop approach improves on the state-of-the-art by 17. 3% for pixel accuracy and 22. 6% for mIoU; and by 10. 6% and 19. 1% when 10 point labels per image are available.
Understanding the Effects of Projectors in Knowledge Distillation
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.
Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection
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.
CVB: A Video Dataset of Cattle Visual Behaviors
Therefore, we introduce a new dataset, called Cattle Visual Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long, captured in natural lighting conditions, and annotated with eleven visually perceptible behaviors of grazing cattle.
Image Labels Are All You Need for Coarse Seagrass Segmentation
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.
A Capsule Network for Hierarchical Multi-Label Image Classification
Our ML-CapsNet predicts multiple image classes based on a hierarchical class-label tree structure.
A Real-time Edge-AI System for Reef Surveys
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels.
Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery
We propose a point label aware method for propagating labels within superpixel regions to obtain augmented ground truth for training a semantic segmentation model.
The CSIRO Crown-of-Thorn Starfish Detection Dataset
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are underway in an attempt to manage COTS populations to ecologically sustainable levels.
Exploring Deep Neural Networks on Edge TPU
Based on the use of a joint-time-frequency data representation, also known as spectrogram, we explore the trade-off between classification performance and the energy consumed for inference.
DeepSeagrass Dataset
We introduce a dataset of seagrass images collected by a biologist snorkelling in Moreton Bay, Queensland, Australia, as described in our publication: arXiv:2009. 09924.
Deep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation
The key results of this paper is that the joint time-frequency data representation, even when used in conjunction with a relatively basic neural network classifier, can outperform the best cattle activity classifiers reported in the literature.
Multi-species Seagrass Detection and Classification from Underwater Images
Underwater surveys conducted using divers or robots equipped with customized camera payloads can generate a large number of images.
Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets
The prediction of periodical time-series remains challenging due to various types of data distortions and misalignments.
Novel Methods for Activity Classification and Occupany Prediction Enabling Fine-grained HVAC Control
Much of the energy consumption in buildings is due to HVAC systems, which has motivated several recent studies on making these systems more energy- efficient.
