3.6k citations · 10.7k across the 127 of their papers we have counts for
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Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes
Guolei Sun, Hisham Cholakkal, Salman Khan +2
The main requisite for fine-grained recognition task is to focus on subtle discriminative details that make the subordinate classes different from each other. We note that existing…
Towards Partial Supervision for Generic Object Counting in Natural Scenes
Hisham Cholakkal, Guolei Sun, Salman Khan +3
Generic object counting in natural scenes is a challenging computer vision problem. Existing approaches either rely on instance-level supervision or absolute count information to t…
MineGAN: effective knowledge transfer from GANs to target domains with few images
Yaxing Wang, Abel Gonzalez-Garcia, David Berga +3
One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality netw…
Deep Contextual Attention for Human-Object Interaction Detection
Tiancai Wang, Rao Muhammad Anwer, Muhammad Haris Khan +4
Human-object interaction detection is an important and relatively new class of visual relationship detection tasks, essential for deeper scene understanding. Most existing approach…
Mask-Guided Attention Network for Occluded Pedestrian Detection
Yanwei Pang, Jin Xie, Muhammad Haris Khan +3
Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance…
AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces
Muhammad Haris Khan, John McDonagh, Salman Khan +5
Being heavily reliant on animals, it is our ethical obligation to improve their well-being by understanding their needs. Several studies show that animal needs are often expressed…
Learning the Model Update for Siamese Trackers
Lichao Zhang, Abel Gonzalez-Garcia, Joost van de Weijer +2
Siamese approaches address the visual tracking problem by extracting an appearance template from the current frame, which is used to localize the target in the next frame. In gener…
Multi-Modal Fusion for End-to-End RGB-T Tracking
Lichao Zhang, Martin Danelljan, Abel Gonzalez-Garcia +2
We propose an end-to-end tracking framework for fusing the RGB and TIR modalities in RGB-T tracking. Our baseline tracker is DiMP (Discriminative Model Prediction), which employs a…
3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization
Sanath Narayan, Hisham Cholakkal, Fahad Shahbaz Khan +1
Temporal action localization is a challenging computer vision problem with numerous real-world applications. Most existing methods require laborious frame-level supervision to trai…
Distilled Siamese Networks for Visual Tracking
Jianbing Shen, Yuanpei Liu, Xingping Dong +3
In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from…
An Adaptive Random Path Selection Approach for Incremental Learning
Jathushan Rajasegaran, Munawar Hayat, Salman Khan +3
In a conventional supervised learning setting, a machine learning model has access to examples of all object classes that are desired to be recognized during the inference stage. T…
Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition
Devraj Mandal, Sanath Narayan, Saikumar Dwivedi +4
Generalized zero-shot action recognition is a challenging problem, where the task is to recognize new action categories that are unavailable during the training stage, in addition…
iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
Syed Waqas Zamir, Aditya Arora, Akshita Gupta +7
Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. In this work, we introduce the first benchmark dataset for instance segmentation i…
Cross-Domain Transferability of Adversarial Perturbations
Muzammal Naseer, Salman H. Khan, Harris Khan +2
Adversarial examples reveal the blind spots of deep neural networks (DNNs) and represent a major concern for security-critical applications. The transferability of adversarial exam…
Discriminative Online Learning for Fast Video Object Segmentation
Andreas Robinson, Felix Järemo Lawin, Martin Danelljan +2
We address the highly challenging problem of video object segmentation. Given only the initial mask, the task is to segment the target in the subsequent frames. In order to effecti…
Learning Digital Camera Pipeline for Extreme Low-Light Imaging
Syed Waqas Zamir, Aditya Arora, Salman Khan +2
In low-light conditions, a conventional camera imaging pipeline produces sub-optimal images that are usually dark and noisy due to a low photon count and low signal-to-noise ratio…