70 citations · 299 across the 35 of their papers we have counts for
17 papers · 1 filter
Guidance Through Surrogate: Towards a Generic Diagnostic Attack
Muzammal Naseer, Salman Khan, Fatih Porikli +1
Adversarial training is an effective approach to make deep neural networks robust against adversarial attacks. Recently, different adversarial training defenses are proposed that n…
UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation
Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed +3
Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is…
Fine-tuned CLIP Models are Efficient Video Learners
Hanoona Rasheed, Muhammad Uzair Khattak, Muhammad Maaz +2
Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP model. Since training on a similar scale for videos is infeasible, recent approaches fo…
Person Image Synthesis via Denoising Diffusion Model
Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal +4
The pose-guided person image generation task requires synthesizing photorealistic images of humans in arbitrary poses. The existing approaches use generative adversarial networks t…
An Investigation into Whitening Loss for Self-supervised Learning
Xi Weng, Lei Huang, Lei Zhao +3
A desirable objective in self-supervised learning (SSL) is to avoid feature collapse. Whitening loss guarantees collapse avoidance by minimizing the distance between embeddings of…
MaPLe: Multi-modal Prompt Learning
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz +2
Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. However, they are sensitive to the choice of input text promp…
Transformers in Remote Sensing: A Survey
Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Muhammad Anwer +4
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformers-based architectures,…
AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility
Mubashir Noman, Wafa Al Ghallabi, Daniya Najiha +7
One of the key factors behind the recent success in visual tracking is the availability of dedicated benchmarks. While being greatly benefiting to the tracking research, existing b…
3D Vision with Transformers: A Survey
Jean Lahoud, Jiale Cao, Fahad Shahbaz Khan +4
The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field. The transformer has been used as a replace…
OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning
Mamshad Nayeem Rizve, Navid Kardan, Salman Khan +2
Semi-supervised learning (SSL) is one of the dominant approaches to address the annotation bottleneck of supervised learning. Recent SSL methods can effectively leverage a large re…
Self-Distilled Vision Transformer for Domain Generalization
Maryam Sultana, Muzammal Naseer, Muhammad Haris Khan +2
In the recent past, several domain generalization (DG) methods have been proposed, showing encouraging performance, however, almost all of them build on convolutional neural networ…
Adversarial Pixel Restoration as a Pretext Task for Transferable Perturbations
Hashmat Shadab Malik, Shahina K Kunhimon, Muzammal Naseer +2
Transferable adversarial attacks optimize adversaries from a pretrained surrogate model and known label space to fool the unknown black-box models. Therefore, these attacks are res…
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection
Hanoona Rasheed, Muhammad Maaz, Muhammad Uzair Khattak +2
Existing open-vocabulary object detectors typically enlarge their vocabulary sizes by leveraging different forms of weak supervision. This helps generalize to novel objects at infe…
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications
Muhammad Maaz, Abdelrahman Shaker, Hisham Cholakkal +4
In the pursuit of achieving ever-increasing accuracy, large and complex neural networks are usually developed. Such models demand high computational resources and therefore cannot…
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
Yawei Li, Kai Zhang, Radu Timofte +108
This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-reso…
Energy-based Latent Aligner for Incremental Learning
K J Joseph, Salman Khan, Fahad Shahbaz Khan +2
Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks. This behavior emerges because the parameter updates optimized for the new tasks…
Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer
Omkar Thawakar, Sanath Narayan, Jiale Cao +6
State-of-the-art transformer-based video instance segmentation (VIS) approaches typically utilize either single-scale spatio-temporal features or per-frame multi-scale features dur…