Search Results for author: Nick Barnes

Found 54 papers, 23 papers with code

Semi-supervised Salient Object Detection with Effective Confidence Estimation

no code implementations28 Dec 2021 Jiawei Liu, Jing Zhang, Nick Barnes

The success of existing salient object detection models relies on a large pixel-wise labeled training dataset.

Object Detection Salient Object Detection

Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction

no code implementations NeurIPS 2021 Jing Zhang, Jianwen Xie, Nick Barnes, Ping Li

In this paper, we take a step further by proposing a novel generative vision transformer with latent variables following an informative energy-based prior for salient object detection.

RGB-D Salient Object Detection Saliency Prediction +1

Learning To Segment Dominant Object Motion From Watching Videos

no code implementations28 Nov 2021 Sahir Shrestha, Mohammad Ali Armin, Hongdong Li, Nick Barnes

Existing deep learning based unsupervised video object segmentation methods still rely on ground-truth segmentation masks to train.

Optical Flow Estimation Semantic Segmentation +2

PU-Transformer: Point Cloud Upsampling Transformer

no code implementations24 Nov 2021 Shi Qiu, Saeed Anwar, Nick Barnes

Given the rapid development of 3D scanners, point clouds are becoming popular in AI-driven machines.

Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model

no code implementations22 Nov 2021 Jing Zhang, Yuchao Dai, Mehrtash Harandi, Yiran Zhong, Nick Barnes, Richard Hartley

Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty.

Object Detection

Inferring the Class Conditional Response Map for Weakly Supervised Semantic Segmentation

1 code implementation27 Oct 2021 Weixuan Sun, Jing Zhang, Nick Barnes

To solve this, most existing approaches follow a multi-training pipeline to refine CAMs for better pseudo-labels, which includes: 1) re-training the classification model to generate CAMs; 2) post-processing CAMs to obtain pseudo labels; and 3) training a semantic segmentation model with the obtained pseudo labels.

Weakly-Supervised Semantic Segmentation

Dense Uncertainty Estimation

1 code implementation13 Oct 2021 Jing Zhang, Yuchao Dai, Mochu Xiang, Deng-Ping Fan, Peyman Moghadam, Mingyi He, Christian Walder, Kaihao Zhang, Mehrtash Harandi, Nick Barnes

Deep neural networks can be roughly divided into deterministic neural networks and stochastic neural networks. The former is usually trained to achieve a mapping from input space to output space via maximum likelihood estimation for the weights, which leads to deterministic predictions during testing.

Decision Making

RGB-D Saliency Detection via Cascaded Mutual Information Minimization

1 code implementation ICCV 2021 Jing Zhang, Deng-Ping Fan, Yuchao Dai, Xin Yu, Yiran Zhong, Nick Barnes, Ling Shao

In this paper, we introduce a novel multi-stage cascaded learning framework via mutual information minimization to "explicitly" model the multi-modal information between RGB image and depth data.

Saliency Detection

PnP-3D: A Plug-and-Play for 3D Point Clouds

1 code implementation16 Aug 2021 Shi Qiu, Saeed Anwar, Nick Barnes

With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis.

Object Detection Semantic Segmentation

Energy-Based Generative Cooperative Saliency Prediction

1 code implementation25 Jun 2021 Jing Zhang, Jianwen Xie, Zilong Zheng, Nick Barnes

Specifically, we propose a generative cooperative saliency prediction framework based on the generative cooperative networks, where a conditional latent variable model and a conditional energy-based model are jointly trained to predict saliency in a cooperative manner.

Saliency Prediction

Confidence-Aware Learning for Camouflaged Object Detection

1 code implementation22 Jun 2021 Jiawei Liu, Jing Zhang, Nick Barnes

Then, we concatenate it with the input image and feed it to the confidence estimation network to produce an one channel confidence map. We generate dynamic supervision for the confidence estimation network, representing the agreement of camouflage prediction with the ground truth camouflage map.

Object Detection

Transformer Transforms Salient Object Detection and Camouflaged Object Detection

no code implementations20 Apr 2021 Yuxin Mao, Jing Zhang, Zhexiong Wan, Yuchao Dai, Aixuan Li, Yunqiu Lv, Xinyu Tian, Deng-Ping Fan, Nick Barnes

Extensive experimental results on various SOD and COD tasks illustrate that transformer networks can transform SOD and COD, leading to new benchmarks for each related task.

Camouflaged Object Segmentation Machine Translation +3

Learning structure-aware semantic segmentation with image-level supervision

1 code implementation15 Apr 2021 Jiawei Liu, Jing Zhang, Yicong Hong, Nick Barnes

Within this pipeline, the class activation map (CAM) is obtained and further processed to serve as a pseudo label to train the semantic segmentation model in a fully-supervised manner.

Boundary Detection Common Sense Reasoning +1

Weakly Supervised Video Salient Object Detection

1 code implementation CVPR 2021 Wangbo Zhao, Jing Zhang, Long Li, Nick Barnes, Nian Liu, Junwei Han

Significant performance improvement has been achieved for fully-supervised video salient object detection with the pixel-wise labeled training datasets, which are time-consuming and expensive to obtain.

Salient Object Detection Video Saliency Detection +1

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion

1 code implementation CVPR 2021 Shi Qiu, Saeed Anwar, Nick Barnes

Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation.

3D Semantic Segmentation

Simultaneously Localize, Segment and Rank the Camouflaged Objects

1 code implementation CVPR 2021 Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, Deng-Ping Fan

With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects.

Object Detection

Recursive Training for Zero-Shot Semantic Segmentation

no code implementations26 Feb 2021 Ce Wang, Moshiur Farazi, Nick Barnes

We propose a recursive training scheme to supervise the retraining of a semantic segmentation model for a zero-shot setting using a pseudo-feature representation.

Semantic Segmentation

Robust normalizing flows using Bernstein-type polynomials

no code implementations6 Feb 2021 Sameera Ramasinghe, Kasun Fernando, Salman Khan, Nick Barnes

Modeling real-world distributions can often be challenging due to sample data that are subjected to perturbations, e. g., instrumentation errors, or added random noise.

Uncertainty-Aware Deep Calibrated Salient Object Detection

no code implementations10 Dec 2020 Jing Zhang, Yuchao Dai, Xin Yu, Mehrtash Harandi, Nick Barnes, Richard Hartley

Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy.

Object Detection Salient Object Detection

3D Guided Weakly Supervised Semantic Segmentation

no code implementations1 Dec 2020 Weixuan Sun, Jing Zhang, Nick Barnes

In this paper, we propose a weakly supervised 2D semantic segmentation model by incorporating sparse bounding box labels with available 3D information, which is much easier to obtain with advanced sensors.

2D Semantic Segmentation Weakly-Supervised Semantic Segmentation

Rethinking conditional GAN training: An approach using geometrically structured latent manifolds

1 code implementation NeurIPS 2021 Sameera Ramasinghe, Moshiur Farazi, Salman Khan, Nick Barnes, Stephen Gould

Conditional GANs (cGAN), in their rudimentary form, suffer from critical drawbacks such as the lack of diversity in generated outputs and distortion between the latent and output manifolds.

Image-to-Image Translation Translation

Conditional Generative Modeling via Learning the Latent Space

no code implementations ICLR 2021 Sameera Ramasinghe, Kanchana Ranasinghe, Salman Khan, Nick Barnes, Stephen Gould

Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-modal settings.

Attention Guided Semantic Relationship Parsing for Visual Question Answering

no code implementations5 Oct 2020 Moshiur Farazi, Salman Khan, Nick Barnes

Humans explain inter-object relationships with semantic labels that demonstrate a high-level understanding required to perform complex Vision-Language tasks such as Visual Question Answering (VQA).

Question Answering Visual Question Answering

Uncertainty Inspired RGB-D Saliency Detection

4 code implementations7 Sep 2020 Jing Zhang, Deng-Ping Fan, Yuchao Dai, Saeed Anwar, Fatemeh Saleh, Sadegh Aliakbarian, Nick Barnes

Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution.

RGB-D Salient Object Detection RGB Salient Object Detection +1

Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection

no code implementations ECCV 2020 Jing Zhang, Jianwen Xie, Nick Barnes

The proposed model consists of two sub-models parameterized by neural networks: (1) a saliency predictor that maps input images to clean saliency maps, and (2) a noise generator, which is a latent variable model that produces noises from Gaussian latent vectors.

Saliency Detection

Attention Based Real Image Restoration

no code implementations26 Apr 2020 Saeed Anwar, Nick Barnes, Lars Petersson

Furthermore, the evaluation in terms of quantitative metrics and visual quality for four restoration tasks i. e. Denoising, Super-resolution, Raindrop Removal, and JPEG Compression on 11 real degraded datasets against more than 30 state-of-the-art algorithms demonstrate the superiority of our R$^2$Net.

Denoising Image Restoration +2

A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers

1 code implementation24 Mar 2020 Saeed Anwar, Nick Barnes, Lars Petersson

In this work, we investigate the performance of the landmark general CNN classifiers, which presented top-notch results on large scale classification datasets, on the fine-grained datasets, and compare it against state-of-the-art fine-grained classifiers.

General Classification

Reducing the Sim-to-Real Gap for Event Cameras

1 code implementation ECCV 2020 Timo Stoffregen, Cedric Scheerlinck, Davide Scaramuzza, Tom Drummond, Nick Barnes, Lindsay Kleeman, Robert Mahony

We present strategies for improving training data for event based CNNs that result in 20-40% boost in performance of existing state-of-the-art (SOTA) video reconstruction networks retrained with our method, and up to 15% for optic flow networks.

Video Reconstruction

Any-Shot Object Detection

no code implementations16 Mar 2020 Shafin Rahman, Salman Khan, Nick Barnes, Fahad Shahbaz Khan

Any-shot detection offers unique challenges compared to conventional novel object detection such as, a high imbalance between unseen, few-shot and seen object classes, susceptibility to forget base-training while learning novel classes and distinguishing novel classes from the background.

Object Detection

Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models

no code implementations20 Jan 2020 Moshiur R. Farazi, Salman H. Khan, Nick Barnes

However, modelling the visual and semantic features in a high dimensional (joint embedding) space is computationally expensive, and more complex models often result in trivial improvements in the VQA accuracy.

Question Answering Visual Question Answering

Spectral-GANs for High-Resolution 3D Point-cloud Generation

1 code implementation4 Dec 2019 Sameera Ramasinghe, Salman Khan, Nick Barnes, Stephen Gould

Point-clouds are a popular choice for vision and graphics tasks due to their accurate shape description and direct acquisition from range-scanners.

Point Cloud Generation

Representation Learning on Unit Ball with 3D Roto-Translational Equivariance

no code implementations30 Nov 2019 Sameera Ramasinghe, Salman Khan, Nick Barnes, Stephen Gould

In this work, we propose a novel `\emph{volumetric convolution}' operation that can effectively model and convolve arbitrary functions in $\mathbb{B}^3$.

3D Object Recognition Representation Learning

Question-Agnostic Attention for Visual Question Answering

no code implementations9 Aug 2019 Moshiur R. Farazi, Salman H. Khan, Nick Barnes

Visual Question Answering (VQA) models employ attention mechanisms to discover image locations that are most relevant for answering a specific question.

Question Answering Visual Question Answering

Densely Residual Laplacian Super-Resolution

1 code implementation28 Jun 2019 Saeed Anwar, Nick Barnes

Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images.

Image Super-Resolution

Unsupervised Primitive Discovery for Improved 3D Generative Modeling

no code implementations CVPR 2019 Salman H. Khan, Yulan Guo, Munawar Hayat, Nick Barnes

Using the primitive parts for shapes as attributes, a parameterized 3D representation is modeled in the first stage.

3D Shape Generation

CED: Color Event Camera Dataset

no code implementations24 Apr 2019 Cedric Scheerlinck, Henri Rebecq, Timo Stoffregen, Nick Barnes, Robert Mahony, Davide Scaramuzza

Event cameras are novel, bio-inspired visual sensors, whose pixels output asynchronous and independent timestamped spikes at local intensity changes, called 'events'.

Event-based vision Image Reconstruction

Real Image Denoising with Feature Attention

3 code implementations ICCV 2019 Saeed Anwar, Nick Barnes

Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling.

Color Image Denoising Image Denoising

A Deep Journey into Super-resolution: A survey

2 code implementations16 Apr 2019 Saeed Anwar, Salman Khan, Nick Barnes

Deep convolutional networks based super-resolution is a fast-growing field with numerous practical applications.

Image Super-Resolution

Asynchronous Spatial Image Convolutions for Event Cameras

no code implementations2 Dec 2018 Cedric Scheerlinck, Nick Barnes, Robert Mahony

In this paper, we propose a method to compute the convolution of a linear spatial kernel with the output of an event camera.

Polarity Loss for Zero-shot Object Detection

2 code implementations22 Nov 2018 Shafin Rahman, Salman Khan, Nick Barnes

This setting gives rise to the need for correct alignment between visual and semantic concepts, so that the unseen objects can be identified using only their semantic attributes.

Metric Learning Zero-Shot Learning +1

Continuous-time Intensity Estimation Using Event Cameras

no code implementations1 Nov 2018 Cedric Scheerlinck, Nick Barnes, Robert Mahony

Event cameras provide asynchronous, data-driven measurements of local temporal contrast over a large dynamic range with extremely high temporal resolution.

Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation

1 code implementation27 Apr 2018 Salman H. Khan, Munawar Hayat, Nick Barnes

Our model simultaneously learns to match the data, reconstruction loss and the latent distributions of real and fake images to improve the quality of generated samples.

Image Generation

Deep Texture and Structure Aware Filtering Network for Image Smoothing

no code implementations ECCV 2018 Kaiyue Lu, ShaoDi You, Nick Barnes

Image smoothing is a fundamental task in computer vision, that aims to retain salient structures and remove insignificant textures.

image smoothing

Perceptually Consistent Color-to-Gray Image Conversion

no code implementations6 May 2016 Shaodi You, Nick Barnes, Janine Walker

In this paper, we propose a color to grayscale image conversion algorithm (C2G) that aims to preserve the perceptual properties of the color image as much as possible.

Learning Structured Hough Voting for Joint Object Detection and Occlusion Reasoning

no code implementations CVPR 2013 Tao Wang, Xuming He, Nick Barnes

We propose a structured Hough voting method for detecting objects with heavy occlusion in indoor environments.

Object Detection

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