Search Results for author:
Found 37 papers, 8 papers with code
Pose-Guided Self-Training with Two-Stage Clustering for Unsupervised Landmark Discovery
Second, motivated by the ZeroShot performance, we develop a ULD algorithm based on diffusion features using self-training and clustering which also outperforms prior methods by notable margins.
Unsupervised Landmark Discovery Using Consistency Guided Bottleneck
In the current work, we introduce a consistency-guided bottleneck in an image reconstruction-based pipeline that leverages landmark consistency, a measure of compatibility score with the pseudo-ground truth to generate adaptive heatmaps.
Detection and Localization of Firearm Carriers in Complex Scenes for Improved Safety Measures
By combining these components, our approach achieves exceptional results on a newly proposed dataset.
Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement
The method has resulted in a significant performance improvement, of up to 5. 0% AUC, of state-of-the-art (SOTA) visual trackers.
Learning Structure Aware Deep Spectral Embedding
Spectral Embedding (SE) has often been used to map data points from non-linear manifolds to linear subspaces for the purpose of classification and clustering.
Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification
There has been a surge of research in deep learning models for WSI classification with clinical applications such as cancer detection or prediction of molecular mutations from WSIs.
Single-branch Network for Multimodal Training
With the rapid growth of social media platforms, users are sharing billions of multimedia posts containing audio, images, and text.
Higher-order Sparse Convolutions in Graph Neural Networks
Graph Neural Networks (GNNs) have been applied to many problems in computer sciences.
Face Pyramid Vision Transformer
A novel Face Pyramid Vision Transformer (FPVT) is proposed to learn a discriminative multi-scale facial representations for face recognition and verification.
Data Augmentation for Graph Data: Recent Advancements
Graph Neural Network (GNNs) based methods have recently become a popular tool to deal with graph data because of their ability to incorporate structural information.
Reconstruction of Time-varying Graph Signals via Sobolev Smoothness
In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples.
Lightweight Encoder-Decoder Architecture for Foot Ulcer Segmentation
We developed a model that is similar in spirit to the well-established encoder-decoder and residual convolution neural networks.
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies
In the current study, we propose a robust anomaly detection framework that overcomes such instability by transforming the fundamental role of the discriminator from identifying real vs. fake data to distinguishing good vs. bad quality reconstructions.
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos
Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well as the rare occurrence of anomalous events in the training data.
Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework
Our experiments demonstrate the effectiveness of the proposed framework for handling the missing joints as well as quantification of the occlusion handling capability of the deep neural networks.
Generative Cooperative Learning for Unsupervised Video Anomaly Detection
Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings.
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection
Learning to detect real-world anomalous events using video-level annotations is a difficult task mainly because of the noise present in labels.
Fake Visual Content Detection Using Two-Stream Convolutional Neural Networks
The proposed detector has demonstrated significant performance improvement compared to the current state-of-the-art fake content detectors and fusing the frequency and spatial domain streams has also improved generalization of the detector.
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection
The proposed method obtains83. 03% and 89. 67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.
Masked Linear Regression for Learning Local Receptive Fields for Facial Expression Synthesis
Compared to facial expression recognition, expression synthesis requires a very high-dimensional mapping.
Facial Expression Recognition
Facial Expression Recognition (FER)
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A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels
Anomalous event detection in surveillance videos is a challenging and practical research problem among image and video processing community.
Localizing Firearm Carriers by Identifying Human-Object Pairs
Visual identification of gunmen in a crowd is a challenging problem, that requires resolving the association of a person with an object (firearm).
Cross-modal Speaker Verification and Recognition: A Multilingual Perspective
Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition.
Deep Latent Space Learning for Cross-modal Mapping of Audio and Visual Signals
We quantitatively and qualitatively evaluate the proposed approach on VoxCeleb, a benchmarks audio-visual dataset on a multitude of tasks including cross-modal verification, cross-modal matching, and cross-modal retrieval.
Do Cross Modal Systems Leverage Semantic Relationships?
Our proposed measure evaluates the semantic similarity between the image and text representations in the latent embedding space.
Leveraging Orientation for Weakly Supervised Object Detection with Application to Firearm Localization
The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors.
Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends
In order to overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of different algorithms.
Unsupervised RGBD Video Object Segmentation Using GANs
To handle these challenges we propose a fusion based moving object segmentation algorithm which exploits color as well as depth information using GAN to achieve more accuracy.
Unsupervised Deep Context Prediction for Background Foreground Separation
Furthermore we also evaluated foreground object detection with the fusion of our proposed method and morphological operations.
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches
In the second part of this work, we experimentally evaluate tracking algorithms for robustness in the presence of additive white Gaussian noise.
Comparative Study of ECO and CFNet Trackers in Noisy Environment
Object tracking is one of the most challenging task and has secured significant attention of computer vision researchers in the past two decades.
Histogram of Oriented Principal Components for Cross-View Action Recognition
We propose the Histogram of Oriented Principal Components (HOPC) descriptor that is robust to noise, viewpoint, scale and action speed variations.
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition
In contrast, we directly process the pointclouds and propose a new technique for action recognition which is more robust to noise, action speed and viewpoint variations.
Action Classification with Locality-constrained Linear Coding
We use the Histogram of Oriented Gradient (HOG3D) feature to encode the information in each cell.
Optimizing Auto-correlation for Fast Target Search in Large Search Space
To this end we propose an Efficient Group Size (EGS) algorithm which minimizes the number of similarity computations for a particular search image.
Semi-supervised Spectral Clustering for Image Set Classification
We present an image set classification algorithm based on unsupervised clustering of labeled training and unlabeled test data where labels are only used in the stopping criterion.
Semi-supervised Spectral Clustering for Classification
We propose a Classification Via Clustering (CVC) algorithm which enables existing clustering methods to be efficiently employed in classification problems.
