Search Results for author: Ruizhi Qiao

Found 7 papers, 1 papers with code

Novelty Detection via Contrastive Learning with Negative Data Augmentation

no code implementations18 Jun 2021 Chengwei Chen, Yuan Xie, Shaohui Lin, Ruizhi Qiao, Jian Zhou, Xin Tan, Yi Zhang, Lizhuang Ma

Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.

Contrastive Learning Data Augmentation +2

Contrastive Learning for Compact Single Image Dehazing

2 code implementations CVPR 2021 Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, Lizhuang Ma

In this paper, we propose a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively.

Contrastive Learning Image Dehazing +1

Visually Aligned Word Embeddings for Improving Zero-shot Learning

no code implementations18 Jul 2017 Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel

To overcome this visual-semantic discrepancy, this work proposes an objective function to re-align the distributed word embeddings with visual information by learning a neural network to map it into a new representation called visually aligned word embedding (VAWE).

Semantic Similarity Semantic Textual Similarity +2

Structured Learning of Tree Potentials in CRF for Image Segmentation

no code implementations26 Mar 2017 Fayao Liu, Guosheng Lin, Ruizhi Qiao, Chunhua Shen

In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs.

Semantic Segmentation

Less is more: zero-shot learning from online textual documents with noise suppression

no code implementations CVPR 2016 Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel

Classifying a visual concept merely from its associated online textual source, such as a Wikipedia article, is an attractive research topic in zero-shot learning because it alleviates the burden of manually collecting semantic attributes.

Zero-Shot Learning

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