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Donghui Wang

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4 papers

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AAAI Conference 2021 Conference Paper

Inference Fusion with Associative Semantics for Unseen Object Detection

Yanan Li
Pengyang Li
Han Cui
Donghui Wang

We study the problem of object detection when training and test objects are disjoint, i. e. no training examples of the target classes are available. Existing unseen object detection approaches usually combine generic detection frameworks with a single-path unseen classifier, by aligning object regions with semantic class embeddings. In this paper, inspired from human cognitive experience, we propose a simple but effective dual-path detection model that further explores associative semantics to supplement the basic visual-semantic knowledge transfer. We use a novel target-centric multipleassociation strategy to establish concept associations, to ensure that the predictor generalized to unseen domain can be learned during training. In this way, through a reasonable inference fusion mechanism, those two parallel reasoning paths can strengthen the correlation between seen and unseen objects, thus improving detection performance. Experiments show that our inductive method can significantly boost the performance by 7. 42% over inductive models, and even 5. 25% over transductive models on MSCOCO dataset.

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IJCAI Conference 2020 Conference Paper

Dress like an Internet Celebrity: Fashion Retrieval in Videos

Hongrui Zhao
Jin Yu
Yanan Li
Donghui Wang
Jie Liu
Hongxia Yang
Fei Wu

Nowadays, both online shopping and video sharing have grown exponentially. Although internet celebrities in videos are ideal exhibition for fashion corporations to sell their products, audiences do not always know where to buy fashion products in videos, which is a cross-domain problem called video-to-shop. In this paper, we propose a novel deep neural network, called Detect, Pick, and Retrieval Network (DPRNet), to break the gap between fashion products from videos and audiences. For the video side, we have modified the traditional object detector, which automatically picks out the best object proposals for every commodity in videos without duplication, to promote the performance of the video-to-shop task. For the fashion retrieval side, a simple but effective multi-task loss network obtains new state-of-the-art results on DeepFashion. Extensive experiments conducted on a new large-scale cross-domain video-to-shop dataset shows that DPRNet is efficient and outperforms the state-of-the-art methods on video-to-shop task.

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IJCAI Conference 2018 Conference Paper

Feature Enhancement in Attention for Visual Question Answering

Yuetan Lin
Zhangyang Pang
Donghui Wang
Yueting Zhuang

Attention mechanism has been an indispensable part of Visual Question Answering (VQA) models, due to the importance of its selective ability on image regions and/or question words. However, attention mechanism in almost all the VQA models takes as input the image visual and question textual features, which stem from different sources and between which there exists essential semantic gap. In order to further improve the accuracy of correlation between region and question in attention, we focus on region representation and propose the idea of feature enhancement, which includes three aspects. (1) We propose to leverage region semantic representation which is more consistent with the question representation. (2) We enrich the region representation using features from multiple hierarchies and (3) we refine the semantic representation for richer information. With these three incremental feature enhancement mechanisms, we improve the region representation and achieve better attentive effect and VQA performance. We conduct extensive experiments on the largest VQA v2. 0 benchmark dataset and achieve competitive results without additional training data, and prove the effectiveness of our proposed feature-enhanced attention by visual demonstrations.

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AAAI Conference 2016 Conference Paper

Relational Knowledge Transfer for Zero-Shot Learning

Donghui Wang
Yanan Li
Yuetan Lin
Yueting Zhuang

General zero-shot learning (ZSL) approaches exploit transfer learning via semantic knowledge space. In this paper, we reveal a novel relational knowledge transfer (RKT) mechanism for ZSL, which is simple, generic and effective. RKT resolves the inherent semantic shift problem existing in ZSL through restoring the missing manifold structure of unseen categories via optimizing semantic mapping. It extracts the relational knowledge from data manifold structure in semantic knowledge space based on sparse coding theory. The extracted knowledge is then transferred backwards to generate virtual data for unseen categories in the feature space. On the one hand, the generalizing ability of the semantic mapping function can be enhanced with the added data. On the other hand, the mapping function for unseen categories can be learned directly from only these generated data, achieving inspiring performance. Incorporated with RKT, even simple baseline methods can achieve good results. Extensive experiments on three challenging datasets show prominent performance obtained by RKT, and we obtain 82. 43% accuracy on the Animals with Attributes dataset.

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