A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future

As the most fundamental tasks of computer vision, object detection and segmentation have made tremendous progress in the deep learning era. Due to the expensive manual labeling, the annotated categories in existing datasets are often small-scale and pre-defined, i.e., state-of-the-art detectors and segmentors fail to generalize beyond the closed-vocabulary. To resolve this limitation, the last few years have witnessed increasing attention toward Open-Vocabulary Detection (OVD) and Segmentation (OVS). In this survey, we provide a comprehensive review on the past and recent development of OVD and OVS. To this end, we develop a taxonomy according to the type of task and methodology. We find that the permission and usage of weak supervision signals can well discriminate different methodologies, including: visual-semantic space mapping, novel visual feature synthesis, region-aware training, pseudo-labeling, knowledge distillation-based, and transfer learning-based. The proposed taxonomy is universal across different tasks, covering object detection, semantic/instance/panoptic segmentation, 3D scene and video understanding. In each category, its main principles, key challenges, development routes, strengths, and weaknesses are thoroughly discussed. In addition, we benchmark each task along with the vital components of each method. Finally, several promising directions are provided to stimulate future research

Holistic, Instance-Level Human Parsing

Object parsing -- the task of decomposing an object into its semantic parts -- has traditionally been formulated as a category-level segmentation problem. Consequently, when there are multiple objects in an image, current methods cannot count the number of objects in the scene, nor can they determine which part belongs to which object. We address this problem by segmenting the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to. Moreover, we show how this approach benefits us in obtaining segmentations at coarser granularities as well. Our proposed network is trained end-to-end given detections, and begins with a category-level segmentation module. Thereafter, a differentiable Conditional Random Field, defined over a variable number of instances for every input image, reasons about the identity of each part by associating it with a human detection. In contrast to other approaches, our method can handle the varying number of people in each image and our holistic network produces state-of-the-art results in instance-level part and human segmentation, together with competitive results in category-level part segmentation, all achieved by a single forward-pass through our neural network.Comment: Poster at BMVC 201