Xiaobing Wang

Peng Hao, Weilong Wang, Xiaobing Wang et al.

Scene Graph Generation (SGG) remains a challenging task due to its compositional property. Previous approaches improve prediction efficiency through end-to-end learning. However, these methods exhibit limited performance as they assume unidirectional conditioning between entities and predicates, which restricts effective information interaction. To address this limitation, we propose a novel bidirectional conditioning factorization in a semantic-aligned space for SGG, enabling efficient and generalizable interaction between entities and predicates. Specifically, we introduce an end-to-end scene graph generation model, the Bidirectional Conditioning Transformer (BCTR), to implement this factorization. BCTR consists of two key modules. First, the Bidirectional Conditioning Generator (BCG) performs multi-stage interactive feature augmentation between entities and predicates, enabling mutual enhancement between these predictions. Second, Random Feature Alignment (RFA) is present to regularize feature space by distilling multi-modal knowledge from pre-trained models. Within this regularized feature space, BCG is feasible to capture interaction patterns across diverse relationships during training, and the learned interaction patterns can generalize to unseen but semantically related relationships during inference. Extensive experiments on Visual Genome and Open Image V6 show that BCTR achieves state-of-the-art performance on both benchmarks.

Xiangyu Zhu, Yingying Jiang, Shuli Yang et al.

Scene text detection is a challenging problem in computer vision. In this paper, we propose a novel text detection network based on prevalent object detection frameworks. In order to obtain stronger semantic feature, we adopt ResNet as feature extraction layers and exploit multi-level feature by combining hierarchical convolutional networks. A vertical proposal mechanism is utilized to avoid proposal classification, while regression layer remains working to improve localization accuracy. Our approach evaluated on ICDAR2013 dataset achieves F-measure of 0.91, which outperforms previous state-of-the-art results in scene text detection.

Yingying Jiang, Xiangyu Zhu, Xiaobing Wang et al.

In this paper, we propose a novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images. The framework is based on Faster R-CNN [1] architecture. First, we use the Region Proposal Network (RPN) to generate axis-aligned bounding boxes that enclose the texts with different orientations. Second, for each axis-aligned text box proposed by RPN, we extract its pooled features with different pooled sizes and the concatenated features are used to simultaneously predict the text/non-text score, axis-aligned box and inclined minimum area box. At last, we use an inclined non-maximum suppression to get the detection results. Our approach achieves competitive results on text detection benchmarks: ICDAR 2015 and ICDAR 2013.