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GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification
Md Faraz Kabir Khan, Saeed Anwar, Ghulam Mubashar Hassan
The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before. We intro…
P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds
Ruikai Cui, Shi Qiu, Saeed Anwar +4
Point cloud completion aims to recover the complete shape based on a partial observation. Existing methods require either complete point clouds or multiple partial observations of…
Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps
Muhammad Ibrahim, Naveed Akhtar, Saeed Anwar +2
Precise localization is critical for autonomous vehicles. We present a self-supervised learning method that employs Transformers for the first time for the task of outdoor localiza…
PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning
Jie Hong, Shi Qiu, Weihao Li +4
Point cloud learning is receiving increasing attention. However, most existing point cloud models lack the practical ability to deal with the unavoidable presence of unknown object…
Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion
Ruikai Cui, Shi Qiu, Saeed Anwar +2
Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence. Differing from existing dete…
Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns
Zhenyue Qin, Pan Ji, Dongwoo Kim +3
Skeleton sequences are compact and lightweight. Numerous skeleton-based action recognizers have been proposed to classify human behaviors. In this work, we aim to incorporate compo…
Representation-Centric Survey of Supervised Skeletal Action Recognition and the New Benchmark
Yang Liu, Jiyao Yang, Madhawa Perera +8
3D skeletal action recognition has emerged as a powerful alternative to traditional RGB and depth-based approaches, offering robustness to environmental variations, computational e…
DRT: A Lightweight Single Image Deraining Recursive Transformer
Yuanchu Liang, Saeed Anwar, Yang Liu
Over parameterization is a common technique in deep learning to help models learn and generalize sufficiently to the given task; nonetheless, this often leads to enormous network s…
Pyramidal Attention for Saliency Detection
Tanveer Hussain, Abbas Anwar, Saeed Anwar +2
Salient object detection (SOD) extracts meaningful contents from an input image. RGB-based SOD methods lack the complementary depth clues; hence, providing limited performance for…
Visual Attention Methods in Deep Learning: An In-Depth Survey
Mohammed Hassanin, Saeed Anwar, Ibrahim Radwan +2
Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more o…
PU-Transformer: Point Cloud Upsampling Transformer
Shi Qiu, Saeed Anwar, Nick Barnes
Given the rapid development of 3D scanners, point clouds are becoming popular in AI-driven machines. However, point cloud data is inherently sparse and irregular, causing significa…
PnP-3D: A Plug-and-Play for 3D Point Clouds
Shi Qiu, Saeed Anwar, Nick Barnes
With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis. However, there is great potential for development of these networks s…
Investigating Attention Mechanism in 3D Point Cloud Object Detection
Shi Qiu, Yunfan Wu, Saeed Anwar +1
Object detection in three-dimensional (3D) space attracts much interest from academia and industry since it is an essential task in AI-driven applications such as robotics, autonom…
Survey: Image Mixing and Deleting for Data Augmentation
Humza Naveed, Saeed Anwar, Munawar Hayat +2
Neural networks are prone to overfitting and memorizing data patterns. To avoid over-fitting and enhance their generalization and performance, various methods have been suggested i…
Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network
Yang Liu, Saeed Anwar, Zhenyue Qin +3
The prevalent convolutional neural network (CNN) based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy. Howe…
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition
Zhenyue Qin, Yang Liu, Pan Ji +5
Skeleton sequences are lightweight and compact, and thus are ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract featu…
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
Chongyi Li, Saeed Anwar, Junhui Hou +3
Underwater images suffer from color casts and low contrast due to wavelength- and distance-dependent attenuation and scattering. To solve these two degradation issues, we present a…
Single Underwater Image Restoration by Contrastive Learning
Junlin Han, Mehrdad Shoeiby, Tim Malthus +6
Underwater image restoration attracts significant attention due to its importance in unveiling the underwater world. This paper elaborates on a novel method that achieves state-of-…
Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion
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. Particularly, real point cloud scenes can intu…
Densely Deformable Efficient Salient Object Detection Network
Tanveer Hussain, Saeed Anwar, Amin Ullah +2
Salient Object Detection (SOD) domain using RGB-D data has lately emerged with some current models' adequately precise results. However, they have restrained generalization abiliti…
Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable
Yang Liu, Zhenyue Qin, Saeed Anwar +2
Ever since the advent of AlexNet, designing novel deep neural architectures for different tasks has consistently been a productive research direction. Despite the exceptional perfo…
Uncertainty Inspired RGB-D Saliency Detection
Jing Zhang, Deng-Ping Fan, Yuchao Dai +4
We propose the first stochastic framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection models tr…
Image Colorization: A Survey and Dataset
Saeed Anwar, Muhammad Tahir, Chongyi Li +3
Image colorization estimates RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality. Over the last decade, deep learning techniques for i…
Dense-Resolution Network for Point Cloud Classification and Segmentation
Shi Qiu, Saeed Anwar, Nick Barnes
Point cloud analysis is attracting attention from Artificial Intelligence research since it can be widely used in applications such as robotics, Augmented Reality, self-driving. Ho…