786 citations · 7.3k across the 175 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…
DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images
Yi Zhou, Boyang Wang, Xiaodong He +2
Diabetic retinopathy (DR) is a complication of diabetes that severely affects eyes. It can be graded into five levels of severity according to international protocol. However, opti…
Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition
Biao Qian, Yang Wang, Zhao Zhang +3
Deep convolutional neural networks have largely benefited computer vision tasks. However, the high computational complexity limits their real-world applications. To this end, many…
Semi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval
Jianjun Lei, Yuxin Song, Bo Peng +3
Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain gap between sketches and natural images. How to align abstract sketches and natural images i…
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…
RANet: Ranking Attention Network for Fast Video Object Segmentation
Ziqin Wang, Jun Xu, Li Liu +2
Despite online learning (OL) techniques have boosted the performance of semi-supervised video object segmentation (VOS) methods, the huge time costs of OL greatly restrict their pr…
Fast Large-Scale Discrete Optimization Based on Principal Coordinate Descent
Huan Xiong, Mengyang Yu, Li Liu +3
Binary optimization, a representative subclass of discrete optimization, plays an important role in mathematical optimization and has various applications in computer vision and ma…
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…
Dual-reference Age Synthesis
Yuan Zhou, Bingzhang Hu, and Jun He +2
Age synthesis methods typically take a single image as input and use a specific number to control the age of the generated image. In this paper, we propose a novel framework taking…
ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
Zhijie Zhang, Huazhu Fu, Hang Dai +3
Segmentation is a fundamental task in medical image analysis. However, most existing methods focus on primary region extraction and ignore edge information, which is useful for obt…
Coupled-Projection Residual Network for MRI Super-Resolution
Chun-Mei Feng, Kai Wang, Shijian Lu +3
Magnetic Resonance Imaging(MRI) has been widely used in clinical application and pathology research by helping doctors make more accurate diagnoses. On the other hand, accurate dia…
Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces
Huazhu Fu, Boyang Wang, Jianbing Shen +4
Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated a…
Extreme Points Derived Confidence Map as a Cue For Class-Agnostic Segmentation Using Deep Neural Network
Shadab Khan, Ahmed H. Shahin, Javier Villafruela +2
To automate the process of segmenting an anatomy of interest, we can learn a model from previously annotated data. The learning-based approach uses annotations to train a model tha…
Understanding More about Human and Machine Attention in Deep Neural Networks
Qiuxia Lai, Salman Khan, Yongwei Nie +3
Human visual system can selectively attend to parts of a scene for quick perception, a biological mechanism known as Human attention. Inspired by this, recent deep learning models…
Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image
Jun Xu, Yuan Huang, Ming-Ming Cheng +4
Supervised deep networks have achieved promisingperformance on image denoising, by learning image priors andnoise statistics on plenty pairs of noisy and clean images. Unsupervised…
NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising
Yingkun Hou, Jun Xu, Mingxia Liu +4
Non-local self similarity (NSS) is a powerful prior of natural images for image denoising. Most of existing denoising methods employ similar patches, which is a patch-level NSS pri…
STAR: A Structure and Texture Aware Retinex Model
Jun Xu, Yingkun Hou, Dongwei Ren +5
Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives a…
Interpretable Neural Network Decoupling
Yuchao Li, Rongrong Ji, Shaohui Lin +5
The remarkable performance of convolutional neural networks (CNNs) is entangled with their huge number of uninterpretable parameters, which has become the bottleneck limiting the e…
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…
Dynamically Visual Disambiguation of Keyword-based Image Search
Yazhou Yao, Zeren Sun, Fumin Shen +6
Due to the high cost of manual annotation, learning directly from the web has attracted broad attention. One issue that limits their performance is the problem of visual polysemy.…
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…