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Found 38 papers, 5 papers with code
Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNN
To address this problem, incremental learning methods have been explored which preserve the old knowledge of deep learning models.
Few-Shot Class-Incremental Learning from an Open-Set Perspective
The continual appearance of new objects in the visual world poses considerable challenges for current deep learning methods in real-world deployments.
Deep Instance-Level Hard Negative Mining Model for Histopathology Images
Histopathology image analysis can be considered as a Multiple instance learning (MIL) problem, where the whole slide histopathology image (WSI) is regarded as a bag of instances (i. e, patches) and the task is to predict a single class label to the WSI.
TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition
While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD).
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry.
Convex Class Model on Symmetric Positive Definite Manifolds
Our proposed framework, named Manifold Convex Class Model, represents each class on SPD manifolds using a convex model, and classification can be performed by computing distances to the convex models.
SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks
In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way.
Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks
We show that existing adversarial perturbation methods are not effective to perform such an attack, especially when there are multiple faces in the input image.
What is the Best Way for Extracting Meaningful Attributes from Pictures?
With this metric, automatic quantitative evaluation can be performed on the attribute sets; thus, reducing the enormous effort to perform manual evaluation.
Determining the best attributes for surveillance video keywords generation
In this paper, we propose a novel approach, based on the shared structure exhibited amongst meaningful attributes, that enables us to compare between different automatic attribute discovery approaches. We then validate our approach by comparing various attribute discovery methods such as PiCoDeS on two attribute datasets.
Automatic and Quantitative evaluation of attribute discovery methods
In our evaluation, we gleaned some insights that could be beneficial in developing automatic attribute discovery methods to generate meaningful attributes.
Efficient Clustering on Riemannian Manifolds: A Kernelised Random Projection Approach
We then validate our framework on several computer vision applications by comparing against popular clustering methods on Riemannian manifolds.
Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients
In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification.
Discovering Discriminative Cell Attributes for HEp-2 Specimen Image Classification
In our work, a specimen image descriptor is represented by its overall cell attribute descriptors.
MRF-based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos
Robust foreground object segmentation via background modelling is a difficult problem in cluttered environments, where obtaining a clear view of the background to model is almost impossible.
Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching
This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol.
Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition
In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence.
K-Tangent Spaces on Riemannian Manifolds for Improved Pedestrian Detection
For covariance-based image descriptors, taking into account the curvature of the corresponding feature space has been shown to improve discrimination performance.
Random Projections on Manifolds of Symmetric Positive Definite Matrices for Image Classification
Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance.
Matching Image Sets via Adaptive Multi Convex Hull
We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set.
Object Tracking via Non-Euclidean Geometry: A Grassmann Approach
A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream.
Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution
Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry.
Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference
While existing face recognition systems based on local features are robust to issues such as misalignment, they can exhibit accuracy degradation when comparing images of differing resolutions.
Classification of Human Epithelial Type 2 Cell Indirect Immunofluoresence Images via Codebook Based Descriptors
In this paper, we propose a cell classification system comprised of a dual-region codebook-based descriptor, combined with the Nearest Convex Hull Classifier.
Shadow Detection: A Survey and Comparative Evaluation of Recent Methods
Furthermore, we propose the use of tracking performance as an unbiased approach for determining the practical usefulness of shadow detection methods.
Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture
The motion and size features are modelled by an approximated version of kernel density estimation, which is computationally efficient even for large training datasets.
Video Face Matching using Subset Selection and Clustering of Probabilistic Multi-Region Histograms
This paper presents a video face recognition system based on probabilistic Multi-Region Histograms to characterise performance trade-offs in: (i) selecting a subset of faces compared to using all faces, and (ii) combining information from all faces via clustering.
Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition
We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors.
Ranked #2 on
Hand Gesture Recognition
on Cambridge
Combined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification
In contrast to comparing faces via single exemplars, matching sets of face images increases robustness and discrimination performance.
Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces
We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold.
Domain Adaptation on the Statistical Manifold
Here, we propose to make better use of the structure of this manifold and rely on the distance on the manifold to compare the source and target distributions.
CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels
This is because the system can ignore the attention mechanism by assigning equal weights for all members.
To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?
Critical findings are observed: (1) The best balance between detection accuracy, detection speed and file size is achieved at 8 times downsampling captured with a $40\times$ objective; (2) compression which reduces the file size dramatically, does not necessarily have an adverse effect on overall accuracy; (3) reducing the amount of training data to some extents causes a drop in precision but has a negligible impact on the recall; (4) in most cases, Faster R-CNN achieves the best accuracy in the glomerulus detection task.
Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation
To achieve gradient alignment, we propose Forward-Backward Cyclic Adaptation, which iteratively computes adaptation from source to target via backward hopping and from target to source via forward passing.
SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation
Incremental learning requires a model to continually learn new tasks from streaming data.
Scalable Bayesian Deep Learning with Kernel Seed Networks
To address this problem we present a method for performing BDL, namely Kernel Seed Networks (KSN), which does not require a 2-fold increase in the number of parameters.
DIODE: Dilatable Incremental Object Detection
In this paper, we aim to alleviate this performance decay on multi-step incremental detection tasks by proposing a dilatable incremental object detector (DIODE).
Multivariate Prototype Representation for Domain-Generalized Incremental Learning
Deep learning models suffer from catastrophic forgetting when being fine-tuned with samples of new classes.
