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...
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,...
摘要: retrieval aims to capture the semantic correspondence between images and texts, which serves as a foundation and crucial component in multi-modal recommendations, search systems,...关键词: image-text retrieval cross-modal retrieval multi-task learning graph convolutional network ...
Keyword(s):conceptual metaphor;conceptual system about Trianon;cultural cognition of Trianon;image metaphor;Treaty of Trianon Most Cited Toward a constructional framework for research on language change Author(s):Elizabeth Closs Traugott Nominal grounding and English quantifiers ...
Wilder incorporates three filmic characters to represent Image, Word, and Motion as the metacinematic cornerstones of film after the advent of talkies. On the surface, the film deals with the major paradigmatic change in the media landscape that took place in the late 1920s and early 1930s ...
Using a pre-trained grounding model that connects phrases to pictures (trained on the densely annotated Visual Genome data set [20]), it generates possible descriptions of parts of a picture which are then used to ground the caption to the image, i.e., it checks if everything described in...
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...