Xudong Lin Columbia University, Gedas Bertasius Facebook AI, Jue Wang Facebook AI, Shih-Fu Chang Columbia University, Devi Parikh Facebook AI and Georgia Tech, Lorenzo Torresani Facebook AI and Dartmouth Vx2Text: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs Accordingly, we establish a new state-of-the-art for scalable object detection. Meanwhile, objects with bounding box annotation can be detected almost as accurately as supervised methods, which is significantly better than weakly supervised baselines. We show that the proposed method can detect and localize objects for which no bounding box annotation is provided during training, at a significantly higher accuracy than zero-shot approaches. We propose a new method to train object detectors using bounding box annotations for a limited set of object categories, as well as image-caption pairs that cover a larger variety of objects at a significantly lower cost. In this paper, we put forth a novel formulation of the object detection problem, namely open-vocabulary object detection, which is more general, more practical, and more effective than weakly supervised and zero-shot approaches. Weakly supervised and zero-shot learning techniques have been explored to scale object detectors to more categories with less supervision, but they have not been as successful and widely adopted as supervised models. Particularly, learning more object categories typically requires proportionally more bounding box annotations. and Columbia University, Kevin Dela Rosa Snap Inc., Derek Hao Hu Snap Inc., Shih-Fu Chang Columbia UniversityÄespite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Open-Vocabulary Object Detection Using CaptionsĪlireza Zareian Snap Inc. The annual event explores machine learning, artificial intelligence, and computer vision research and its applications. Research from the department has been accepted to the 2021 Computer Vision and Pattern Recognition (CVPR) Conference.
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