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Found 14 papers, 4 papers with code
A Novel Boundary Matching Algorithm for Video Temporal Error Concealment
It then uses a classic boundary matching criterion or the proposed boundary matching criterion adaptively to identify matching distortion in each boundary of candidate MB.
Deep Learning for Visual Tracking: A Comprehensive Survey
Second, popular visual tracking benchmarks and their respective properties are compared, and their evaluation metrics are summarized.
Effective Fusion of Deep Multitasking Representations for Robust Visual Tracking
Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters.
Beyond Background-Aware Correlation Filters: Adaptive Context Modeling by Hand-Crafted and Deep RGB Features for Visual Tracking
In recent years, the background-aware correlation filters have achie-ved a lot of research interest in the visual target tracking.
Efficient Scale Estimation Methods using Lightweight Deep Convolutional Neural Networks for Visual Tracking
In recent years, visual tracking methods that are based on discriminative correlation filters (DCF) have been very promising.
COMET: Context-Aware IoU-Guided Network for Small Object Tracking
To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy.
Adaptive Exploitation of Pre-trained Deep Convolutional Neural Networks for Robust Visual Tracking
Third, the generalization of the proposed method is validated on various tracking datasets as well as CNN models with similar architectures.
CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture Search
A strong visual object tracker nowadays relies on its well-crafted modules, which typically consist of manually-designed network architectures to deliver high-quality tracking results.
Learning-based Monocular 3D Reconstruction of Birds: A Contemporary Survey
In nature, the collective behavior of animals, such as flying birds is dominated by the interactions between individuals of the same species.
Classification of Bark Beetle-Induced Forest Tree Mortality using Deep Learning
Bark beetle outbreaks can dramatically impact forest ecosystems and services around the world.
Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review
This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three primary perspectives: bark beetle & host interactions, RS, and ML/DL.
Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Images
Visual explanation of ``black-box'' models allows researchers in explainable artificial intelligence (XAI) to interpret the model's decisions in a human-understandable manner.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Training-based Model Refinement and Representation Disagreement for Semi-Supervised Object Detection
Extensive experiments demonstrate the superior performance of our approach over state-of-the-art SSOD methods.
ShadowSense: Unsupervised Domain Adaptation and Feature Fusion for Shadow-Agnostic Tree Crown Detection from RGB-Thermal Drone Imagery
Accurate detection of individual tree crowns from remote sensing data poses a significant challenge due to the dense nature of forest canopy and the presence of diverse environmental variations, e. g., overlapping canopies, occlusions, and varying lighting conditions.
