3.6k citations · 10.7k across the 127 of their papers we have counts for
21 papers · 1 filter
D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations
Sanath Narayan, Hisham Cholakkal, Munawar Hayat +3
This work proposes a weakly-supervised temporal action localization framework, called D2-Net, which strives to temporally localize actions using video-level supervision. Our main c…
Learning to Fuse Asymmetric Feature Maps in Siamese Trackers
Wencheng Han, Xingping Dong, Fahad Shahbaz Khan +2
Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCor…
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
Mariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu +3
Anomaly detection in video is a challenging computer vision problem. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods…
Synthesizing the Unseen for Zero-shot Object Detection
Nasir Hayat, Munawar Hayat, Shafin Rahman +3
The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during in…
Meta-learning the Learning Trends Shared Across Tasks
Jathushan Rajasegaran, Salman Khan, Munawar Hayat +2
Meta-learning stands for 'learning to learn' such that generalization to new tasks is achieved. Among these methods, Gradient-based meta-learning algorithms are a specific sub-clas…
From Handcrafted to Deep Features for Pedestrian Detection: A Survey
Jiale Cao, Yanwei Pang, Jin Xie +2
Pedestrian detection is an important but challenging problem in computer vision, especially in human-centric tasks. Over the past decade, significant improvement has been witnessed…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Fahad Shahbaz Khan +2
Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. The complexity of the task arises from the commonly…
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…
SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation
Jiale Cao, Rao Muhammad Anwer, Hisham Cholakkal +3
Single-stage instance segmentation approaches have recently gained popularity due to their speed and simplicity, but are still lagging behind in accuracy, compared to two-stage met…
Stylized Adversarial Defense
Muzammal Naseer, Salman Khan, Munawar Hayat +2
Deep Convolution Neural Networks (CNNs) can easily be fooled by subtle, imperceptible changes to the input images. To address this vulnerability, adversarial training creates pertu…
A Self-supervised Approach for Adversarial Robustness
Muzammal Naseer, Salman Khan, Munawar Hayat +2
Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems e.g., for classification, segmentation and object detection. The vulnerabili…
Self-supervised Knowledge Distillation for Few-shot Learning
Jathushan Rajasegaran, Salman Khan, Munawar Hayat +2
Real-world contains an overwhelmingly large number of object classes, learning all of which at once is infeasible. Few shot learning is a promising learning paradigm due to its abi…
Semi-supervised Learning for Few-shot Image-to-Image Translation
Yaxing Wang, Salman Khan, Abel Gonzalez-Garcia +2
In the last few years, unpaired image-to-image translation has witnessed remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely…
Learning Human-Object Interaction Detection using Interaction Points
Tiancai Wang, Tong Yang, Martin Danelljan +3
Understanding interactions between humans and objects is one of the fundamental problems in visual classification and an essential step towards detailed scene understanding. Human-…
Learning Fast and Robust Target Models for Video Object Segmentation
Andreas Robinson, Felix Järemo Lawin, Martin Danelljan +2
Video object segmentation (VOS) is a highly challenging problem since the initial mask, defining the target object, is only given at test-time. The main difficulty is to effectivel…
iTAML: An Incremental Task-Agnostic Meta-learning Approach
Jathushan Rajasegaran, Salman Khan, Munawar Hayat +2
Humans can continuously learn new knowledge as their experience grows. In contrast, previous learning in deep neural networks can quickly fade out when they are trained on a new ta…
CycleISP: Real Image Restoration via Improved Data Synthesis
Syed Waqas Zamir, Aditya Arora, Salman Khan +4
The availability of large-scale datasets has helped unleash the true potential of deep convolutional neural networks (CNNs). However, for the single-image denoising problem, captur…
Any-Shot Object Detection
Shafin Rahman, Salman Khan, Nick Barnes +1
Previous work on novel object detection considers zero or few-shot settings where none or few examples of each category are available for training. In real world scenarios, it is l…
Incremental Object Detection via Meta-Learning
K J Joseph, Jathushan Rajasegaran, Salman Khan +2
In a real-world setting, object instances from new classes can be continuously encountered by object detectors. When existing object detectors are applied to such scenarios, their…
PSC-Net: Learning Part Spatial Co-occurrence for Occluded Pedestrian Detection
Jin Xie, Yanwei Pang, Hisham Cholakkal +3
Detecting pedestrians, especially under heavy occlusions, is a challenging computer vision problem with numerous real-world applications. This paper introduces a novel approach, te…