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Found 41 papers, 20 papers with code
IPR-NeRF: Ownership Verification meets Neural Radiance Field
Neural Radiance Field (NeRF) models have gained significant attention in the computer vision community in the recent past with state-of-the-art visual quality and produced impressive demonstrations.
InteractDiffusion: Interaction Control in Text-to-Image Diffusion Models
While recent advancements have introduced control over factors such as object localization, posture, and image contours, a crucial gap remains in our ability to control the interactions between objects in the generated content.
Everyone Can Attack: Repurpose Lossy Compression as a Natural Backdoor Attack
The vulnerabilities to backdoor attacks have recently threatened the trustworthiness of machine learning models in practical applications.
Unsupervised Hashing with Similarity Distribution Calibration
However, these methods often overlook the fact that the similarity between data points in the continuous feature space may not be preserved in the discrete hash code space, due to the limited similarity range of hash codes.
An Embarrassingly Simple Approach for Intellectual Property Rights Protection on Recurrent Neural Networks
Capitalise on deep learning models, offering Natural Language Processing (NLP) solutions as a part of the Machine Learning as a Service (MLaaS) has generated handsome revenues.
Extremely Low-light Image Enhancement with Scene Text Restoration
Deep learning-based methods have made impressive progress in enhancing extremely low-light images - the image quality of the reconstructed images has generally improved.
ACORT: A Compact Object Relation Transformer for Parameter Efficient Image Captioning
Recent research that applies Transformer-based architectures to image captioning has resulted in state-of-the-art image captioning performance, capitalising on the success of Transformers on natural language tasks.
End-to-End Supermask Pruning: Learning to Prune Image Captioning Models
With the advancement of deep models, research work on image captioning has led to a remarkable gain in raw performance over the last decade, along with increasing model complexity and computational cost.
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
In this work, we propose a novel deep hashing model with only a single learning objective.
ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment
With hundreds of thousands of electronic chip components are being manufactured every day, chip manufacturers have seen an increasing demand in seeking a more efficient and effective way of inspecting the quality of printed texts on chip components.
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks
Ever since Machine Learning as a Service emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties.
Ternary Hashing
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts.
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack
Ever since Machine Learning as a Service (MLaaS) emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties.
Protect, Show, Attend and Tell: Empowering Image Captioning Models with Ownership Protection
By and large, existing Intellectual Property (IP) protection on deep neural networks typically i) focus on image classification task only, and ii) follow a standard digital watermarking framework that was conventionally used to protect the ownership of multimedia and video content.
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks.
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering
Visual Question Answering (VQA) methods have made incredible progress, but suffer from a failure to generalize.
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect these inventions from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners.
ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT
Robust text reading from street view images provides valuable information for various applications.
[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect these inventions from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners.
ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)
This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting.
Image Captioning with Sparse Recurrent Neural Network
Recurrent Neural Network (RNN) has been widely used to tackle a wide variety of language generation problems and are capable of attaining state-of-the-art (SOTA) performance.
Digital Passport: A Novel Technological Strategy for Intellectual Property Protection of Convolutional Neural Networks
In order to prevent deep neural networks from being infringed by unauthorized parties, we propose a generic solution which embeds a designated digital passport into a network, and subsequently, either paralyzes the network functionalities for unauthorized usages or maintain its functionalities in the presence of a verified passport.
COMIC: Towards A Compact Image Captioning Model with Attention
This is because the size of word and output embedding matrices grow proportionally with the size of vocabulary, adversely affecting the compactness of these networks.
A Universal Logic Operator for Interpretable Deep Convolution Networks
Explaining neural network computation in terms of probabilistic/fuzzy logical operations has attracted much attention due to its simplicity and high interpretability.
Getting to Know Low-light Images with The Exclusively Dark Dataset
Thus, we propose the Exclusively Dark dataset to elevate this data drought, consisting exclusively of ten different types of low-light images (i. e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations.
Phrase-based Image Captioning with Hierarchical LSTM Model
Automatic generation of caption to describe the content of an image has been gaining a lot of research interests recently, where most of the existing works treat the image caption as pure sequential data.
Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition
Text in curve orientation, despite being one of the common text orientations in real world environment, has close to zero existence in well received scene text datasets such as ICDAR2013 and MSRA-TD500.
Ranked #27 on
Scene Text Detection
on Total-Text
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
Namely, we focus on theories, tools and challenges for the RS community.
Improved ArtGAN for Conditional Synthesis of Natural Image and Artwork
Qualitatively, we demonstrate that ArtGAN is able to generate plausible-looking images on Oxford-102 and CUB-200, as well as able to draw realistic artworks based on style, artist, and genre.
ArtGAN: Artwork Synthesis with Conditional Categorical GANs
This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ARTGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics.
phi-LSTM: A Phrase-based Hierarchical LSTM Model for Image Captioning
The two levels of this model are dedicated to i) learn to generate image relevant noun phrases, and ii) produce appropriate image description from the phrases and other words in the corpus.
Crowd Behavior Analysis: A Review where Physics meets Biology
Although the traits emerged in a mass gathering are often non-deliberative, the act of mass impulse may lead to irre- vocable crowd disasters.
Deep-Plant: Plant Identification with convolutional neural networks
This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England.
Fuzzy human motion analysis: A review
Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on.
Detection of Salient Regions in Crowded Scenes
The increasing number of cameras and a handful of human operators to monitor the video inputs from hundreds of cameras leave the system ill equipped to fulfil the task of detecting anomalies.
Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding
Image understanding is an important research domain in the computer vision due to its wide real-world applications.
Refined Particle Swarm Intelligence Method for Abrupt Motion Tracking
Conventional tracking solutions are not feasible in handling abrupt motion as they are based on smooth motion assumption or an accurate motion model.
Zero-Shot Object Recognition System based on Topic Model
Object recognition systems usually require fully complete manually labeled training data to train the classifier.
Scene Image is Non-Mutually Exclusive - A Fuzzy Qualitative Scene Understanding
In this paper, we show that scene images are non-mutually exclusive, and propose the Fuzzy Qualitative Rank Classifier (FQRC) to tackle the aforementioned problems.
Crowd Saliency Detection via Global Similarity Structure
This paper proposes a novel framework to identify and localize salient regions in a crowd scene, by transforming low-level features extracted from crowd motion field into a global similarity structure.
A Fusion Approach for Efficient Human Skin Detection
A reliable human skin detection method that is adaptable to different human skin colours and illu- mination conditions is essential for better human skin segmentation.
