The class representes metadata for a single image in the dataset, including its annotations. Usage annotation_meta_image = metadata.images[0] Key attrbiutes annotation_file: str: A name of the metadata annotation file. coco_image: CocoImage: COCO-style annotated image. labels: Dict[str, str...
Grounding Tokens. For each grounded text entity denoted with a bounding box, we represent the location information as l = [αmin, βmin, αmax, βmax] with its top-left and bottom- right coordinates. For the text entity e, we use the same pre- trained text encoder to obtain it...
Image caption requires that features not only should represent objects, but also are able to reflect the relationship between objects. For example, in Fig. 1, for classification, the features only need to represent some objects, such as “baby”, “table” and “cake”. However, when ...
have inves- tigated text to 3D scene generation [4, 5]; other approaches to image synthesis include stochastic grammars [20], prob- abalistic programming [27], inverse graphics [28], neural de-rendering [55], and generative ConvNets [56]. Scene Graphs represent scenes as directed graphs,...
We noticed that these problems do not potentiate one another, but actually combine to represent an actionable path towards a fully automated object detection pipeline. By leveraging SAM and GroundingDINO with supporting text models, we can automatically annotate images without manual intervention. Then,...
to multiple objects (e.g., shirt vs person example), but the output will still make sense and represent at least one of those objects in an image. This type of pre-training algorithm can be used as a general methodology for zero-shot transfer to downstream segmentation tasks via "...
We illustrate our model here for the case of a Hasse diagram, using typed first-order logic to formalise the image schemas and to represent the geometry of a diagram. The latter additionally requires the use of some qualitative spatial reasoning formalisms. We show that, by blending image ...
GroundingMetadata HarmCategory HyperparameterTuningJob Overview LabelsEntry IdMatcher ImportDataConfig Overview AnnotationLabelsEntry DataItemLabelsEntry ImportDataOperationMetadata ImportDataRequest ImportDataResponse ImportFeatureValuesOperationMetadata ImportFeatureValuesR...
Similarly to most state-of-the-art methods on our target datasets, we represent image regions using a Fast RCNN network [8] fine- tuned on the union of PASCAL 2007 and 2012 trainval sets [5]. The only excep- tion is the experiment reported in Table 1(d), where we fine-tune the ...
which is a task of identifying the fine-grained correspondence between phrases in a sentence and objects (or regions) in an image, is an effective and scalable pre-training task to learn an object- level, language-aware, and semantic-rich visual representa- tion, and propose Grounded Language...