Detecting objects as multiple keypoints is an important approach in the anchor-free object detection methods while corner pooling is an effective feature encoding method for corner positioning. The corners of the bounding box are located by summing the feature maps which are max-pooled in the x and y directions respectively by corner pooling. In the unidirectional max pooling operation, the features of the densely arranged objects of the same class are prone to occlusion. To this end, we propose a method named Gradient Corner Pooling. The spatial distance information of objects on the feature map is encoded during the unidirectional pooling process, which effectively alleviates the occlusion of the homogeneous object features. Further, the computational complexity of gradient corner pooling is the same as traditional corner pooling and hence it can be implemented efficiently. Gradient corner pooling obtains consistent improvements for various keypoint-based methods by directly replacing corner pooling. We verify the gradient corner pooling algorithm on the dataset and in real scenarios, respectively. The networks with gradient corner pooling located the corner points earlier in the training process and achieve an average accuracy improvement of 0.2%-1.6% on the MS-COCO dataset. The detectors with gradient corner pooling show better angle adaptability for arrayed objects in the actual scene test.

6D pose estimation from a single RGB image is a fundamental task in computer vision. In most methods of instance-level or category-level 6D pose estimation, accurate CAD models or point cloud models are indispensable part. It is not easy to quickly obtain the models of these everyday objects. To address this issue, we present a part-level object component sketch knowledge base which consists of 270 real-world object sketch models of 30 categories. Objects are disassembled into geometry components with spatial relationship according to their functions and structures, and convert them into three basic spatial structures: frustum, circular truncated cone, and sphere. We present a fast pipeline for sketch modeling with our tool. The average time for this method to build a simple model for everyday objects is about 2 minutes. Additionally, we leverage the geometric information and spatial relationships inherent in the multiple viewpoint projection maps of these sketch bases to develop a rapid inference framework for 6D pose estimation. The interpretable steps in our framework gradually retrieve and activate valid solutions in the discrete 6D pose space. Extensive experiments in real-world environments have demonstrated that our method can reliably and robustly estimate the 6D pose of objects, even without access to accurate CAD or point cloud models. Furthermore, our method achieves state-of-the-art performance, operating at a speed of 90 frames per second using parallel computing on GPU.

Writer retrieval is crucial in document forensics and historical document analysis. However, due to the difference in syntactic structure between Chinese and other languages, the existing methods may not be directly applied to Chinese writer retrieval. Previous work on Chinese writer retrieval does not overcome the performance degradation problem when the number of samples grows. In this paper, we propose a novel compound uniform encoding algorithm (CUE) for Chinese writer retrieval, which mainly consists of a combined feature extraction module (CFE) and a prototype substitution module (PS). The CFE module combines two complementary features from image filter response and character contour. It counts local symmetries and edge co-occurrence pairs. PS module substitutes the outliers with the class prototypes to alleviate the influence of the outliers. Finally, the weighted Chi-square distance is applied to measure the similarity between writer and text. To verify the superiority of our proposed method, experiments are conducted on four public datasets and our built dataset. The results validate that CUE outperforms the state-of-the-art algorithms on mAP metric.