2.7k citations · 7.3k across the 172 of their papers we have counts for
11 papers · 2 filters
Real-time Semantic Image Segmentation via Spatial Sparsity
Zifeng Wu, Chunhua Shen, Anton van den Hengel
We propose an approach to semantic (image) segmentation that reduces the computational costs by a factor of 25 with limited impact on the quality of results. Semantic segmentation…
Visual Question Answering as a Meta Learning Task
Damien Teney, Anton van den Hengel
The predominant approach to Visual Question Answering (VQA) demands that the model represents within its weights all of the information required to answer any question about any im…
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards
Junjie Zhang, Qi Wu, Chunhua Shen +3
Despite significant progress in a variety of vision-and-language problems, developing a method capable of asking intelligent, goal-oriented questions about images is proven to be a…
Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning
Qi Wu, Peng Wang, Chunhua Shen +2
The Visual Dialogue task requires an agent to engage in a conversation about an image with a human. It represents an extension of the Visual Question Answering task in that the age…
Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries
Bohan Zhuang, Qi Wu, Chunhua Shen +2
Recognising objects according to a pre-defined fixed set of class labels has been well studied in the Computer Vision. There are a great many practical applications where the subje…
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
Peter Anderson, Qi Wu, Damien Teney +6
A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot…
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
Damien Teney, Peter Anderson, Xiaodong He +1
This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the 2017 VQA Challenge. VQA is a task of significant importance for r…
Beyond Low Rank: A Data-Adaptive Tensor Completion Method
Lei Zhang, Wei Wei, Qinfeng Shi +3
Low rank tensor representation underpins much of recent progress in tensor completion. In real applications, however, this approach is confronted with two challenging problems, nam…
When Unsupervised Domain Adaptation Meets Tensor Representations
Hao Lu, Lei Zhang, Zhiguo Cao +4
Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another. It is thus of great practical importa…
Visually Aligned Word Embeddings for Improving Zero-shot Learning
Ruizhi Qiao, Lingqiao Liu, Chunhua Shen +1
Zero-shot learning (ZSL) highly depends on a good semantic embedding to connect the seen and unseen classes. Recently, distributed word embeddings (DWE) pre-trained from large text…
Care about you: towards large-scale human-centric visual relationship detection
Bohan Zhuang, Qi Wu, Chunhua Shen +2
Visual relationship detection aims to capture interactions between pairs of objects in images. Relationships between objects and humans represent a particularly important subset of…