b'Salman Khan'

research

∙ 04/22/2022

Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging

We propose a novel self-supervised Video Object Segmentation (VOS) appro...

jyoti-kini, et al. ∙

research

∙ 03/28/2022

Energy-based Latent Aligner for Incremental Learning

Deep learning models tend to forget their earlier knowledge while increm...

K J Joseph, et al. ∙

research

∙ 03/24/2022

Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer

State-of-the-art transformer-based video instance segmentation (VIS) app...

Omkar Thawakar, et al. ∙

research

∙ 01/24/2022

Transformers in Medical Imaging: A Survey

Following unprecedented success on the natural language tasks, Transform...

Fahad Shamshad, et al. ∙

research

∙ 01/10/2022

Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing

In an autonomous driving system, perception - identification of features...

Izzeddin Teeti, et al. ∙

research

∙ 01/07/2022

A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items

Screening cluttered and occluded contraband items from baggage X-ray sca...

Taimur Hassan, et al. ∙

research

∙ 12/09/2021

Spatio-temporal Relation Modeling for Few-shot Action Recognition

We propose a novel few-shot action recognition framework, STRM, which en...

Anirudh Thatipelli, et al. ∙

research

∙ 12/06/2021

DoodleFormer: Creative Sketch Drawing with Transformers

Creative sketching or doodling is an expressive activity, where imaginat...

Ankan Kumar Bhunia, et al. ∙

research

∙ 12/02/2021

Self-supervised Video Transformer

In this paper, we propose self-supervised training for video transformer...

Kanchana Ranasinghe, et al. ∙

research

∙ 12/02/2021

OW-DETR: Open-world Detection Transformer

Open-world object detection (OWOD) is a challenging computer vision prob...

Akshita Gupta, et al. ∙

research

∙ 10/27/2021

International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines

The aim of this paper is to formalize a new continual semi-supervised le...

Ajmal Shahbaz, et al. ∙

research

∙ 08/22/2021

Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition

Automated systems designed for screening contraband items from the X-ray...

Taimur Hassan, et al. ∙

research

∙ 08/20/2021

Discriminative Region-based Multi-Label Zero-Shot Learning

Multi-label zero-shot learning (ZSL) is a more realistic counter-part of...

Sanath Narayan, et al. ∙

research

∙ 07/23/2021

Learning Discriminative Representations for Multi-Label Image Recognition

Multi-label recognition is a fundamental, and yet is a challenging task ...

Mohammed Hassanin, et al. ∙

research

∙ 07/15/2021

Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition

Identifying potential threats concealed within the baggage is of prime c...

Taimur Hassan, et al. ∙

research

∙ 06/08/2021

On Improving Adversarial Transferability of Vision Transformers

Vision transformers (ViTs) process input images as sequences of patches ...

Muzammal Naseer, et al. ∙

research

∙ 04/26/2021

Rich Semantics Improve Few-shot Learning

Human learning benefits from multi-modal inputs that often appear as ric...

Mohamed Afham, et al. ∙

research

∙ 04/16/2021

Spatiotemporal Deformable Models for Long-Term Complex Activity Detection

Long-term complex activity recognition and localisation can be crucial f...

Salman Khan, et al. ∙

research

∙ 04/08/2021

Handwriting Transformers

We propose a novel transformer-based styled handwritten text image gener...

Ankan Kumar Bhunia, et al. ∙

research

∙ 03/26/2021

On Generating Transferable Targeted Perturbations

While the untargeted black-box transferability of adversarial perturbati...

Muzammal Naseer, et al. ∙

research

∙ 03/25/2021

Orthogonal Projection Loss

Deep neural networks have achieved remarkable performance on a range of ...

Kanchana Ranasinghe, et al. ∙

research

∙ 03/03/2021

Towards Open World Object Detection

Humans have a natural instinct to identify unknown object instances in t...

K J Joseph, et al. ∙

research

∙ 03/01/2021

Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning

In many real-world problems, collecting a large number of labeled sample...

Mamshad Nayeem Rizve, et al. ∙

research

∙ 02/23/2021

ROAD: The ROad event Awareness Dataset for Autonomous Driving

Humans approach driving in a holistic fashion which entails, in particul...

Gurkirt Singh, et al. ∙

research

∙ 02/06/2021

Robust normalizing flows using Bernstein-type polynomials

Normalizing flows (NFs) are a class of generative models that allows exa...

Sameera Ramasinghe, et al. ∙

research

∙ 01/27/2021

Generative Multi-Label Zero-Shot Learning

Multi-label zero-shot learning strives to classify images into multiple ...

Akshita Gupta, et al. ∙

research

∙ 01/04/2021

Transformers in Vision: A Survey

Astounding results from transformer models on natural language tasks hav...

Salman Khan, et al. ∙

research

∙ 11/25/2020

How to train your conditional GAN: An approach using geometrically structured latent manifolds

Conditional generative modeling typically requires capturing one-to-many...

Sameera Ramasinghe, et al. ∙

research

∙ 10/19/2020

Synthesizing the Unseen for Zero-shot Object Detection

The existing zero-shot detection approaches project visual features to t...

Nasir Hayat, et al. ∙

research

∙ 10/19/2020

Meta-learning the Learning Trends Shared Across Tasks

Meta-learning stands for 'learning to learn' such that generalization to...

Jathushan Rajasegaran, et al. ∙

research

∙ 10/07/2020

Conditional Generative Modeling via Learning the Latent Space

Although deep learning has achieved appealing results on several machine...

Sameera Ramasinghe, et al. ∙

research

∙ 10/05/2020

Attention Guided Semantic Relationship Parsing for Visual Question Answering

Humans explain inter-object relationships with semantic labels that demo...

Moshiur Farazi, et al. ∙

research

∙ 09/28/2020

Trainable Structure Tensors for Autonomous Baggage Threat Detection Under Extreme Occlusion

Detecting baggage threats is one of the most difficult tasks, even for e...

Taimur Hassan, et al. ∙

research

∙ 09/25/2020

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

This paper introduces the real image Super-Resolution (SR) challenge tha...

Pengxu Wei, et al. ∙

research

∙ 07/29/2020

Stylized Adversarial Defense

Deep Convolution Neural Networks (CNNs) can easily be fooled by subtle, ...

Muzammal Naseer, et al. ∙

research

∙ 06/17/2020

Self-supervised Knowledge Distillation for Few-shot Learning

Real-world contains an overwhelmingly large number of object classes, le...

Jathushan Rajasegaran, et al. ∙

research

∙ 06/08/2020

A Self-supervised Approach for Adversarial Robustness

Adversarial examples can cause catastrophic mistakes in Deep Neural Netw...

Muzammal Naseer, et al. ∙

research

∙ 04/14/2020

Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans

In the last two decades, baggage scanning has globally become one of the...

Taimur Hassan, et al. ∙

research

∙ 03/30/2020

Semi-supervised Learning for Few-shot Image-to-Image Translation

In the last few years, unpaired image-to-image translation has witnessed...

Yaxing Wang, et al. ∙

research

∙ 03/25/2020

iTAML: An Incremental Task-Agnostic Meta-learning Approach

Humans can continuously learn new knowledge as their experience grows. I...

Jathushan Rajasegaran, et al. ∙

research

∙ 03/17/2020

CycleISP: Real Image Restoration via Improved Data Synthesis

The availability of large-scale datasets has helped unleash the true pot...

Syed Waqas Zamir, et al. ∙

research

∙ 03/17/2020

Incremental Object Detection via Meta-Learning

In a real-world setting, object instances from new classes may be contin...

K J Joseph, et al. ∙

research

∙ 03/16/2020

Any-Shot Object Detection

Previous work on novel object detection considers zero or few-shot setti...

Shafin Rahman, et al. ∙

research

∙ 03/15/2020

Learning Enriched Features for Real Image Restoration and Enhancement

With the goal of recovering high-quality image content from its degraded...

Syed Waqas Zamir, et al. ∙

research

∙ 12/14/2019

Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes

The main requisite for fine-grained recognition task is to focus on subt...

Guolei Sun, et al. ∙

research

∙ 12/13/2019

Towards Partial Supervision for Generic Object Counting in Natural Scenes

Generic object counting in natural scenes is a challenging computer visi...

Hisham Cholakkal, et al. ∙

research

∙ 12/04/2019

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

Point-clouds are a popular choice for vision and graphics tasks due to t...

Sameera Ramasinghe, et al. ∙

research

∙ 11/30/2019

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

Convolution is an integral operation that defines how the shape of one f...

Sameera Ramasinghe, et al. ∙

research

∙ 09/11/2019

AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces

Being heavily reliant on animals, it is our ethical obligation to improv...

Muhammad Haris Khan, et al. ∙

research

∙ 08/24/2019

Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes

Existing networks directly learn feature representations on 3D point clo...

Sameera Ramasinghe, et al. ∙

Salman Khan

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