Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks418 from public data
- Image-Based Recommendations on Styles and Substitutes3,040
The approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within.
- Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments2,014
This work provides the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset and presents the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery.
- Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection1,828
The proposed memory-augmented autoencoder called MemAE is free of assumptions on the data type and thus general to be applied to different tasks and proves the excellent generalization and high effectiveness of the proposed MemAE.
- Wider or Deeper: Revisiting the ResNet Model for Visual Recognition1,757
This paper starts from a group of relatively shallow networks, which perform as well or even better than the current state-of-the-art models on the ImageNet classification dataset, and initialize fully convolutional networks (FCNs) using pre-trained models, and tune them for semantic image segmentation.
- Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation941
This work shows how to improve semantic segmentation through the use of contextual information, specifically, ' patch-patch' context between image regions, and 'patch-background' context, and formulate Conditional Random Fields with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches.
- REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs918
The Retinal Fundus Glaucoma Challenge, REFUGE, was held in conjunction with MICCAI 2018 and it was observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task.
- A survey of appearance models in visual object tracking771
This survey provides a detailed review of the existing 2D appearance models for visual object tracking and takes a module-based architecture that enables readers to easily grasp the key points ofVisual object tracking.
- 647
- FVQA: Fact-Based Visual Question Answering566
A conventional visual question answering dataset is extended, which contains image-question-answer triplets, through additional image- question-answer-supporting fact tuples, and a novel model is described which is capable of reasoning about an image on the basis of supporting-facts.
- REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments551
A dataset of varied and complex robot tasks, described in natural language, in terms of objects visible in a large set of real images, and a novel Interactive Navigator-Pointer model is proposed that provides a strong baseline on the task.
- Deep Anomaly Detection with Deviation Networks490
A novel anomaly detection framework and its instantiation that can be trained substantially more data-efficiently and achieves significantly better anomaly scoring than state-of-the-art competing methods is introduced.
- Learning to rank in person re-identification with metric ensembles481
This work proposes an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated and formulates two optimization algorithms, which directly optimize evaluation measures commonly used in person re -identification.
- Visual question answering: A survey of methods and datasets476
The state of the art by comparing modern approaches to VQA, and the common approach of combining convolutional and recurrent neural networks to map images and questions to a common feature space are examined.
- What Value Do Explicit High Level Concepts Have in Vision to Language Problems?464
A method of incorporating high-level concepts into the successful CNN-RNN approach is proposed, and it is shown that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering.
- Graph-Structured Representations for Visual Question Answering459
This paper proposes to build graphs over the scene objects and over the question words, and describes a deep neural network that exploits the structure in these representations, and achieves significant improvements over the state-of-the-art.
- From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur439
This work directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow and is the first universal end-to-end mapping from the blur image to the dense motion flow.
- Fast Supervised Hashing with Decision Trees for High-Dimensional Data430
Experiments demonstrate that the proposed method significantly outperforms most state-of-the-art methods in retrieval precision and training time, and is orders of magnitude faster than many methods in terms of training time.
- Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge403
This work presents a massive exploration of the effects of the myriad architectural and hyperparameter choices that must be made in generating a state-of-the-art model and provides a detailed analysis of the impact of each choice on model performance.
- Image Captioning and Visual Question Answering Based on Attributes and External Knowledge401
A visual question answering model that combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions and allows questions to be asked where the image alone does not contain the information required to select the appropriate answer.
- Ask Me Anything: Free-Form Visual Question Answering Based on Knowledge from External Sources392
A method for visual question answering which combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions and is specifically able to answer questions posed in natural language, that refer to information not contained in the image.
- Attention-Guided Network for Ghost-Free High Dynamic Range Imaging313
The proposed AHDRNet is a non-flow-based method, which can also avoid the artifacts generated by optical-flow estimation error, and can achieve state-of-the-art quantitative and qualitative results.
- Explicit Knowledge-based Reasoning for Visual Question Answering295
A method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base is described, addressing one of the key issues in general visual answering.
- 287
- Neighbourhood Watch: Referring Expression Comprehension via Language-Guided Graph Attention Networks281
A graph-based, language-guided attention mechanism that represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches, and enables the comprehension decision to be visualizable and explainable.
- Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection257
This work devise an end-to-end trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data and outperforms state-of-the-art methods that require no labeled training data.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.