Search Results for author: Ranga Rodrigo

Found 31 papers, 12 papers with code

Moving Object Based Collision-Free Video Synopsis

no code implementations17 Sep 2023 Anton Jeran Ratnarajah, Sahani Goonetilleke, Dumindu Tissera, Kapilan Balagopalan, Ranga Rodrigo

Video synopsis, summarizing a video to generate a shorter video by exploiting the spatial and temporal redundancies, is important for surveillance and archiving.

Object

Forensic Video Analytic Software

no code implementations17 Sep 2023 Anton Jeran Ratnarajah, Sahani Goonetilleke, Dumindu Tissera, Kapilan Balagopalan, Ranga Rodrigo

This project has resulted in three research outcomes Moving Object Based Collision Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture and Tampering Detection Inter-Frame Forgery.

Activity Recognition Anomaly Detection +4

SATHUR: Self Augmenting Task Hallucinal Unified Representation for Generalized Class Incremental Learning

no code implementations13 Aug 2023 Sathursan Kanagarajah, Thanuja Ambegoda, Ranga Rodrigo

The inherent ability of GWR to form distinct clusters, each corresponding to a class in the feature vector space, regardless of the order of samples or class imbalances, is well suited to achieving GCIL.

Class Incremental Learning Incremental Learning

3DLatNav: Navigating Generative Latent Spaces for Semantic-Aware 3D Object Manipulation

1 code implementation17 Nov 2022 Amaya Dharmasiri, Dinithi Dissanayake, Mohamed Afham, Isuru Dissanayake, Ranga Rodrigo, Kanchana Thilakarathna

However, most models do not offer controllability to manipulate the shape semantics of component object parts without extensive semantic attribute labels or other reference point clouds.

Attribute Disentanglement +1

Visual-Semantic Contrastive Alignment for Few-Shot Image Classification

no code implementations20 Oct 2022 Mohamed Afham, Ranga Rodrigo

The pre-trained semantic feature extractor (learned from a large-scale text corpora) we use in our approach provides a strong contextual prior knowledge to assist FSL.

Classification Contrastive Learning +2

Realistic, Animatable Human Reconstructions for Virtual Fit-On

no code implementations16 Oct 2022 Gayal Kuruppu, Bumuthu Dilshan, Shehan Samarasinghe, Nipuna Madhushan, Ranga Rodrigo

We present an end-to-end virtual try-on pipeline, that can fit different clothes on a personalized 3-D human model, reconstructed using a single RGB image.

Virtual Try-on

DualCam: A Novel Benchmark Dataset for Fine-grained Real-time Traffic Light Detection

1 code implementation3 Sep 2022 Harindu Jayarathne, Tharindu Samarakoon, Hasara Koralege, Asitha Divisekara, Ranga Rodrigo, Peshala Jayasekara

We introduce a novel benchmark traffic light dataset captured using a synchronized pair of narrow-angle and wide-angle cameras covering urban and semi-urban roads.

Navigate Self-Driving Cars

Dynamic Template Initialization for Part-Aware Person Re-ID

no code implementations24 Aug 2022 Kalana Abeywardena, Shechem Sumanthiran, Sanoojan Baliah, Nadarasar Bahavan, Nalith Udugampola, Ajith Pasqual, Chamira Edussooriya, Ranga Rodrigo

Many of the existing Person Re-identification (Re-ID) approaches depend on feature maps which are either partitioned to localize parts of a person or reduced to create a global representation.

Person Re-Identification

HPGNN: Using Hierarchical Graph Neural Networks for Outdoor Point Cloud Processing

no code implementations5 Jun 2022 Arulmolivarman Thieshanthan, Amashi Niwarthana, Pamuditha Somarathne, Tharindu Wickremasinghe, Ranga Rodrigo

Inspired by recent improvements in point cloud processing for autonomous navigation, we focus on using hierarchical graph neural networks for processing and feature learning over large-scale outdoor LiDAR point clouds.

Autonomous Navigation Semantic Segmentation

Towards Real-time Traffic Sign and Traffic Light Detection on Embedded Systems

1 code implementation5 May 2022 Oshada Jayasinghe, Sahan Hemachandra, Damith Anhettigama, Shenali Kariyawasam, Tharindu Wickremasinghe, Chalani Ekanayake, Ranga Rodrigo, Peshala Jayasekara

In this work, we propose a simple deep learning based end-to-end detection framework, which effectively tackles challenges inherent to traffic sign and traffic light detection such as small size, large number of classes and complex road scenarios.

PointCaps: Raw Point Cloud Processing using Capsule Networks with Euclidean Distance Routing

no code implementations21 Dec 2021 Dishanika Denipitiyage, Vinoj Jayasundara, Ranga Rodrigo, Chamira U. S. Edussooriya

We address these limitations in existing capsule network based approaches by proposing PointCaps, a novel convolutional capsule architecture with parameter sharing.

Segmentation

KORSAL: Key-point Detection based Online Real-Time Spatio-Temporal Action Localization

1 code implementation5 Nov 2021 Kalana Abeywardena, Shechem Sumanthiran, Sakuna Jayasundara, Sachira Karunasena, Ranga Rodrigo, Peshala Jayasekara

Despite the simplicity of our approach, our lightweight end-to-end architecture achieves state-of-the-art frame-mAP of 74. 7% on the challenging UCF101-24 dataset, demonstrating a performance gain of 6. 4% over the previous best online methods.

Optical Flow Estimation Spatio-Temporal Action Localization +1

SwiftLane: Towards Fast and Efficient Lane Detection

no code implementations22 Oct 2021 Oshada Jayasinghe, Damith Anhettigama, Sahan Hemachandra, Shenali Kariyawasam, Ranga Rodrigo, Peshala Jayasekara

Recent work done on lane detection has been able to detect lanes accurately in complex scenarios, yet many fail to deliver real-time performance specifically with limited computational resources.

Lane Detection

CeyMo: See More on Roads -- A Novel Benchmark Dataset for Road Marking Detection

1 code implementation22 Oct 2021 Oshada Jayasinghe, Sahan Hemachandra, Damith Anhettigama, Shenali Kariyawasam, Ranga Rodrigo, Peshala Jayasekara

In this paper, we introduce a novel road marking benchmark dataset for road marking detection, addressing the limitations in the existing publicly available datasets such as lack of challenging scenarios, prominence given to lane markings, unavailability of an evaluation script, lack of annotation formats and lower resolutions.

Instance Segmentation object-detection +3

End-To-End Data-Dependent Routing in Multi-Path Neural Networks

no code implementations6 Jul 2021 Dumindu Tissera, Rukshan Wijessinghe, Kasun Vithanage, Alex Xavier, Subha Fernando, Ranga Rodrigo

Having multiple parallel convolutional/dense operations in each layer solves this problem, but without any context-dependent allocation of resources among these operations: the parallel computations tend to learn similar features making the widening process less effective.

Diverse Single Image Generation with Controllable Global Structure

no code implementations9 Feb 2021 Sutharsan Mahendren, Chamira Edussooriya, Ranga Rodrigo

Image generation from a single image using generative adversarial networks is quite interesting due to the realism of generated images.

Image Generation single-image-generation

Fast and Accurate Light Field Saliency Detection through Deep Encoding

no code implementations25 Oct 2020 Sahan Hemachandra, Ranga Rodrigo, Chamira Edussooriya

Light field saliency detection -- important due to utility in many vision tasks -- still lacks speed and can improve in accuracy.

Saliency Detection

Feature-Dependent Cross-Connections in Multi-Path Neural Networks

no code implementations24 Jun 2020 Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe, Kumara Kahatapitiya, Subha Fernando, Ranga Rodrigo

As opposed to conventional network widening, multi-path architectures restrict the quadratic increment of complexity to a linear scale.

Context-Aware Multipath Networks

no code implementations26 Jul 2019 Dumindu Tissera, Kumara Kahatapitiya, Rukshan Wijesinghe, Subha Fernando, Ranga Rodrigo

In view of this, networks which can allocate resources according to the context of the input and regulate flow of information across the network are effective.

Image Classification

Exploiting the Redundancy in Convolutional Filters for Parameter Reduction

1 code implementation26 Jul 2019 Kumara Kahatapitiya, Ranga Rodrigo

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks over the years.

Context-Aware Automatic Occlusion Removal

1 code implementation7 May 2019 Kumara Kahatapitiya, Dumindu Tissera, Ranga Rodrigo

Occlusion removal is an interesting application of image enhancement, for which, existing work suggests manually-annotated or domain-specific occlusion removal.

Image Enhancement

DeepCaps: Going Deeper with Capsule Networks

5 code implementations CVPR 2019 Jathushan Rajasegaran, Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Suranga Seneviratne, Ranga Rodrigo

Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data.

Decoder

TextCaps : Handwritten Character Recognition with Very Small Datasets

3 code implementations17 Apr 2019 Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Jathushan Rajasegaran, Suranga Seneviratne, Ranga Rodrigo

Our system is useful in character recognition for localized languages that lack much labeled training data and even in other related more general contexts such as object recognition.

Few-Shot Image Classification Image Generation

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