While Text-to-Image (T2I) diffusion models excel at generating visually appealing images of individual instances, they struggle to accurately position and control the features generation of multiple instances. The Layout-to-Image (L2I) task was introduced to address the positioning challenges by inco...
2. 引言 首先介绍一下open-set Grounded Text2Img Generation,它是一个框架,它可以根据文本描述和定位指令生成图像。定位指令提供有关图像的附加信息,例如边界框、深度图、语义地图等。所提出的框架可以在不同类型的定位指令上进行训练,例如检测数据、检测+字幕数据和定位数据。该模型在COCO2014数据集上进行评估,同时...
It is important to note that our model GLIGEN is designed for open-world grounded text-to-image generation with caption and various condition inputs (e.g. bounding box). However, we also recognize the importance of responsible AI considerations and the need to clearly communicate the capabilitie...
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything speechimage-editingcaptiondata-generation3d-whole-body-pose-estimationopen-vocabulary-detectionopen-vocabulary-segmentationautomatic-labeling-system ...
4. Open-set Grounded Image Generation 4.1. Grounding Instruction Input For grounded text-to-image generation, there are a vari- ety of ways to ground the generation process via an addi- tional condition. We denote the semantic information of the grounding entity as e, which can be des...
visual information is key to providing context. We present the first example of an image-grounded conversational agent using visual sentiment, facial expression and scene features. We show that key qualities of the generated dialogue can be manipulated by the features used for training the...
Distributed Attention for Grounded Image Captioning 研究了弱监督 grounded image caption 的问题。给定一副图像, 目标是自动生成一个句子来描述图像的上下文, 每个名词对应于图像中相应的区域。 由于缺乏明确的细粒度 region word对齐作为监督,这个任务是具有挑战性的。以往的弱监督方法主要是探索各种正则化方案来提高 ...
Grounded-SAM:github.com/IDEA-Researc Image tagging task: 给一个图片,旨在通过识别给定图像的多个标签来提供语义标签,可以理解为给出多个tags用来形容这个图,包括目标(object)、场景(scene)、属性(attribute)和行为(action),是一个多标签分类(multi-label classification)。 1.RAM模型 包含了三个模块: Image Encode...
However, the status quo is to use text input alone, which can impede controllability. In this work, we propose Gligen, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them ...
4. Training TVSN(Transformation-grounded view synthesis network) First, we need to prepare pretrained vgg16 network. We importedcaffemodeland translated into torch nngraph format. You can download translated version with provided script. $(tvsn_root)/tvsn/code/lossnet$>./download_lossnet.sh ...