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20172022
most citedTask-driven Visual Saliency and Attention-based Visual Question Answering

22 citations · 58 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CV20221 cited

Dual Path Structural Contrastive Embeddings for Learning Novel Objects

Bingbin Li, Elvis Han Cui, Yanan Li +2

Learning novel classes from a very few labeled samples has attracted increasing attention in machine learning areas. Recent research on either meta-learning based or transfer-learn…

cs.CV2021

Fine-grained Semantic Constraint in Image Synthesis

Pengyang Li, Donghui Wang

In this paper, we propose a multi-stage and high-resolution model for image synthesis that uses fine-grained attributes and masks as input. With a fine-grained attribute, the propo…

cs.CV2020

MGD-GAN: Text-to-Pedestrian generation through Multi-Grained Discrimination

Shengyu Zhang, Donghui Wang, Zhou Zhao +3

In this paper, we investigate the problem of text-to-pedestrian synthesis, which has many potential applications in art, design, and video surveillance. Existing methods for text-t…

cs.CV201718 cited

Zero-Shot Learning with Generative Latent Prototype Model

Yanan Li, Donghui Wang

Zero-shot learning, which studies the problem of object classification for categories for which we have no training examples, is gaining increasing attention from community. Most e…

cs.CV201715 cited

Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths

Yanan Li, Donghui Wang, Huanhang Hu +2

Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding sp…

cs.CV201722 cited

Task-driven Visual Saliency and Attention-based Visual Question Answering

Yuetan Lin, Zhangyang Pang, Donghui Wang +1

Visual question answering (VQA) has witnessed great progress since May, 2015 as a classic problem unifying visual and textual data into a system. Many enlightening VQA works explor…