3.6k citations · 5.8k across the 64 of their papers we have counts for
80 papers
Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation
Nian Liu, Kepan Nan, Wangbo Zhao +7
Few-Shot Video Object Segmentation (FSVOS) aims to segment objects in a query video with the same category defined by a few annotated support images. However, this task was seldom…
Unsupervised Landmark Discovery Using Consistency Guided Bottleneck
Mamona Awan, Muhammad Haris Khan, Sanoojan Baliah +4
We study a challenging problem of unsupervised discovery of object landmarks. Many recent methods rely on bottlenecks to generate 2D Gaussian heatmaps however, these are limited in…
Learning Enriched Features for Fast Image Restoration and Enhancement
Syed Waqas Zamir, Aditya Arora, Salman Khan +4
Given a degraded input image, image restoration aims to recover the missing high-quality image content. Numerous applications demand effective image restoration, e.g., computationa…
Transformers in Medical Imaging: A Survey
Fahad Shamshad, Salman Khan, Syed Waqas Zamir +4
Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results a…
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items
Taimur Hassan, Samet Akcay, Mohammed Bennamoun +2
Screening cluttered and occluded contraband items from baggage X-ray scans is a cumbersome task even for the expert security staff. This paper presents a novel strategy that extend…
Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing
Izzeddin Teeti, Valentina Musat, Salman Khan +3
In an autonomous driving system, perception - identification of features and objects from the environment - is crucial. In autonomous racing, high speeds and small margins demand r…
Spatio-temporal Relation Modeling for Few-shot Action Recognition
Anirudh Thatipelli, Sanath Narayan, Salman Khan +3
We propose a novel few-shot action recognition framework, STRM, which enhances class-specific feature discriminability while simultaneously learning higher-order temporal represent…
Self-supervised Video Transformer
Kanchana Ranasinghe, Muzammal Naseer, Salman Khan +2
In this paper, we propose self-supervised training for video transformers using unlabeled video data. From a given video, we create local and global spatiotemporal views with varyi…
OW-DETR: Open-world Detection Transformer
Akshita Gupta, Sanath Narayan, K J Joseph +3
Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown o…
Restormer: Efficient Transformer for High-Resolution Image Restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan +3
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoratio…
Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition
Taimur Hassan, Samet Akcay, Mohammed Bennamoun +2
Automated systems designed for screening contraband items from the X-ray imagery are still facing difficulties with high clutter, concealment, and extreme occlusion. In this paper,…
Discriminative Region-based Multi-Label Zero-Shot Learning
Sanath Narayan, Akshita Gupta, Salman Khan +3
Multi-label zero-shot learning (ZSL) is a more realistic counter-part of standard single-label ZSL since several objects can co-exist in a natural image. However, the occurrence of…
Learning Discriminative Representations for Multi-Label Image Recognition
Mohammed Hassanin, Ibrahim Radwan, Salman Khan +1
Multi-label recognition is a fundamental, and yet is a challenging task in computer vision. Recently, deep learning models have achieved great progress towards learning discriminat…
Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition
Taimur Hassan, Samet Akcay, Mohammed Bennamoun +2
Identifying potential threats concealed within the baggage is of prime concern for the security staff. Many researchers have developed frameworks that can detect baggage threats fr…
Intriguing Properties of Vision Transformers
Muzammal Naseer, Kanchana Ranasinghe, Salman Khan +3
Vision transformers (ViT) have demonstrated impressive performance across various machine vision problems. These models are based on multi-head self-attention mechanisms that can f…
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
Mamshad Nayeem Rizve, Salman Khan, Fahad Shahbaz Khan +1
In many real-world problems, collecting a large number of labeled samples is infeasible. Few-shot learning (FSL) is the dominant approach to address this issue, where the objective…
Handwriting Transformers
Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal +3
We propose a novel transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement as well as global and local writi…
Rich Semantics Improve Few-shot Learning
Mohamed Afham, Salman Khan, Muhammad Haris Khan +2
Human learning benefits from multi-modal inputs that often appear as rich semantics (e.g., description of an object's attributes while learning about it). This enables us to learn…
Orthogonal Projection Loss
Kanchana Ranasinghe, Muzammal Naseer, Munawar Hayat +2
Deep neural networks have achieved remarkable performance on a range of classification tasks, with softmax cross-entropy (CE) loss emerging as the de-facto objective function. The…
Multi-Stage Progressive Image Restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan +4
Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synerg…
On Generating Transferable Targeted Perturbations
Muzammal Naseer, Salman Khan, Munawar Hayat +2
While the untargeted black-box transferability of adversarial perturbations has been extensively studied before, changing an unseen model's decisions to a specific `targeted' class…
Towards Open World Object Detection
K J Joseph, Salman Khan, Fahad Shahbaz Khan +1
Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when t…
ROAD: The ROad event Awareness Dataset for Autonomous Driving
Gurkirt Singh, Stephen Akrigg, Manuele Di Maio +13
Humans drive in a holistic fashion which entails, in particular, understanding dynamic road events and their evolution. Injecting these capabilities in autonomous vehicles can thus…
Robust normalizing flows using Bernstein-type polynomials
Sameera Ramasinghe, Kasun Fernando, Salman Khan +1
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. Since flow m…