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pro vyhledávání: '"object proposal"'
Autor:
Wang, Tinghuai
Learning a data-driven spatio-temporal semantic representation of the objects is the key to coherent and consistent labelling in video. This paper proposes to achieve semantic video object segmentation by learning a data-driven representation which c
Externí odkaz:
http://arxiv.org/abs/2407.05913
Akademický článek
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Autor:
Feinglass, Joshua, Yang, Yezhou
Object proposal generation serves as a standard pre-processing step in Vision-Language (VL) tasks (image captioning, visual question answering, etc.). The performance of object proposals generated for VL tasks is currently evaluated across all availa
Externí odkaz:
http://arxiv.org/abs/2309.00215
Autor:
Xu, Mengmeng, Li, Yanghao, Fu, Cheng-Yang, Ghanem, Bernard, Xiang, Tao, Perez-Rua, Juan-Manuel
This paper deals with the problem of localizing objects in image and video datasets from visual exemplars. In particular, we focus on the challenging problem of egocentric visual query localization. We first identify grave implicit biases in current
Externí odkaz:
http://arxiv.org/abs/2211.10528
Detecting both known and unknown objects is a fundamental skill for robot manipulation in unstructured environments. Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background se
Externí odkaz:
http://arxiv.org/abs/2211.11530
We introduce VIOLA, an object-centric imitation learning approach to learning closed-loop visuomotor policies for robot manipulation. Our approach constructs object-centric representations based on general object proposals from a pre-trained vision m
Externí odkaz:
http://arxiv.org/abs/2210.11339
Autor:
Kreuzberg, Lars, Zulfikar, Idil Esen, Mahadevan, Sabarinath, Engelmann, Francis, Leibe, Bastian
Publikováno v:
European Conference on Computer Vision Workshops 2022
In this work, we present a new paradigm, called 4D-StOP, to tackle the task of 4D Panoptic LiDAR Segmentation. 4D-StOP first generates spatio-temporal proposals using voting-based center predictions, where each point in the 4D volume votes for a corr
Externí odkaz:
http://arxiv.org/abs/2209.14858
Object proposal generation is an important and fundamental task in computer vision. In this paper, we propose ProposalCLIP, a method towards unsupervised open-category object proposal generation. Unlike previous works which require a large number of
Externí odkaz:
http://arxiv.org/abs/2201.06696
Autor:
Wilms, Christian, Frintrop, Simone
Class-agnostic object proposal generation is an important first step in many object detection pipelines. However, object proposals of modern systems are rather inaccurate in terms of segmentation and only roughly adhere to object boundaries. Since ty
Externí odkaz:
http://arxiv.org/abs/2108.03503
Autor:
Wilms, Christian, Frintrop, Simone
Precise segmentation of objects is an important problem in tasks like class-agnostic object proposal generation or instance segmentation. Deep learning-based systems usually generate segmentations of objects based on coarse feature maps, due to the i
Externí odkaz:
http://arxiv.org/abs/2101.04574