open-vocabulary object detection综述 Open-vocabulary object detection refers to the task of detecting and localizing objects in images or videos without relying on a pre-defined set of object categories. In traditional object detection approaches, a fixed set of object categories is predefined, and ...
Open-Vocabulary Object Detection (OVD) tasks have been advanced by enhancing the regional representation capability of the Constrastive Language-Image Pre-training (CLIP). However, as CLIP is trained with image-text pairs, lacking precise object location information, merely enhancing the regional ...
分2阶段,Localized Semantic Matching(LSM)和 Specialized Task Tuning(STT),LSM通过image-region和caption中的word匹配来学到object的语义,STT使用object annotations针对目标检测任务学习视觉特征。 可以理解为LSM通过image-region和word细粒度的对齐 学习语义的视觉特征提取 和RPN Proposal提取模型 及 语义丰富的image embe...
Then we propose a regional prompt learning method to steer the textual latent space towards the task of object detection, i.e., transform the textual embedding space, to better align the visual representation of object-centric images. In addition, we further develop a self-training regime, ...
the first paper which proposes the task of "open-vocabulary object detection" 2 introduction OD:each category needs thousands of bounding boxes; stage 1: use {image, caption} pairs to learn a visual semantic space; stage 2: use annotated boxes for several classes to train object detection; ...
Recently, vision-language pre-training shows great potential in open-vocabulary object detection, where detectors trained on base classes are devised for detecting new classes. The class text embedding is firstly generated by feeding prompts to the text encoder of...
4.3. Direct and Task-Specific Transfer 4.4. Segmentation and Detection in the Wild 4.5. Ablation 5. Conclusion 我们提出了OpenSeeD,这是一个简单的开放式词汇分割和检测框架,它使用单个模型从不同的分割和检测数据集中联合学习。为了弥补前台目标和后台对象之间的任务差距,我们提出了一种基于语言引导的前台查询...
Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects. We ide...
Existing open-vocabulary object detectors typically require a predefined set of categories from users, signifi-cantly confining their application scenarios. In this pa-per, we introduce DetCLIPv3, a high-performing detector that excels not only at both open-vocabulary object detection, but also gene...
Inthispaperweaddressa more realistic version of the natural language groundingtask where we must both identify whether the phrase is rel-evant to an image and localize the phrase. This can also beviewed as a generalization of object detection to an open-ended vocabulary, essentially introducing ...