Search Results for author:
Found 25 papers, 5 papers with code
Zoom-shot: Fast and Efficient Unsupervised Zero-Shot Transfer of CLIP to Vision Encoders with Multimodal Loss
To address this issue, we propose Zoom-shot, a novel method for transferring the zero-shot capabilities of CLIP to any pre-trained vision encoder.
SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions
Lastly, we observed that a maritime object detection model faced challenges in detecting objects in stormy sea backgrounds, emphasizing the impact of weather conditions on detection accuracy.
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024
The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV).
Ranked #1 on
Semantic Segmentation
on LaRS
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion
In this work, we investigate the problem of Model-Agnostic Zero-Shot Classification (MA-ZSC), which refers to training non-specific classification architectures (downstream models) to classify real images without using any real images during training.
Does Interference Exist When Training a Once-For-All Network?
To show this, we propose a simple-yet-effective method called Random Subnet Sampling (RSS), which does not have mitigation on the interference effect.
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.
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.
Deep inspection: an electrical distribution pole parts study via deep neural networks
We propose a novel two-stage zoom-in detection method to gradually focus on the object of interest.
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.
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.
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.
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).
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.
Towards Miss Universe Automatic Prediction: The Evening Gown Competition
Can we predict the winner of Miss Universe after watching how they stride down the catwalk during the evening gown competition?
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.
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions
We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation).
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.
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.
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.
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.
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.
