Text-to-Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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The development of the brain's blood supply in an embryo involves a complex process with several stages. Initially, there is a network of connections between the carotid and basilar artery systems that usually disappear during development. However, in some cases, these connections remain, leading to variations in the adult cerebral circulation. Around the 24th day of embryonic life (at the 3 mm embryonic stage), the internal carotid arteries (ICA) emerge. They are formed from a combination of the 3rd branchial arch arteries and the distal segments of the paired dorsal aortae. The ICA initially provides all the blood needed by the developing brain. As the brain grows, particularly the occipital region, brainstem, and cerebellum, the blood supply from the ICA becomes insufficient, prompting the development of the posterior circulation. At this stage (4-5 mm embryonic stage), the posterior circulation, specifically the hindbrain, is supplied by two longitudinal neural arteries. These arteries receive blood from several temporary connections (anastomoses) with the ICA, including: Trigeminal artery (TA), Otic artery (OA), Hypoglossal artery (HA), Proatlantal artery (ProA). These carotid-vertebrobasilar anastomoses act as temporary bridges, providing blood flow to the developing posterior circulation until the vertebral arteries (VA) and the basilar artery (BA) are fully formed.6
The BA forms between the 5-8 mm stage by the fusion of the longitudinal neural arteries. As the posterior communicating artery develops and connects with the distal BA, the TA, OA, and HA typically regress. Unlike the TA, OA, and HA, the ProA persists until the VA are fully developed. A segment of the ProA becomes incorporated into the V3 segment of the VA and the distal portions of the occipital artery. The VA develop between the 7-12 mm stage from transverse connections between cervical intersegmental arteries, starting with the ProA and progressing downwards to the 6th intersegmental artery. This 6th intersegmental artery eventually forms the origin of the adult VA from the subclavian artery. 7-8
Persistence of the HA into adulthood, the focus of this discussion, is a rare occurrence. It is the second most common persistent carotid-vertebrobasilar anastomosis, following the persistent trigeminal artery. The failure of the HA to regress during embryological development leads to PPHA. This condition can impact the normal blood flow dynamics in the brain and, in certain cases, can be associated with cerebrovascular issues like aneurysms or ischemic events.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sd2001/Image2Caption
https://github.com/sdai654416/Joint-GAN
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/securade/hub
Semantic-Conditional Diffusion Networks for Image Captioning
https://github.com/shangeth/Vehicle_Speed_Estimation
https://github.com/shilin-lu/vine
TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition
https://github.com/showlab/Tune-A-Video
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/showlab/loveu-tgve-2023
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/showlab/show-o
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
https://github.com/sidward14/Style-AttnGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/simnyatsanga/image-caption-generator
https://github.com/sndnyang/Diffusion_ViT
https://github.com/snow-mn/GAN-INT-CLS
https://github.com/soumik12345/Automatic-Number-Plate-Recognition
https://github.com/souvikshanku/digit-captioning
https://github.com/sovit-123/Deep-Learning-Image-Captioning
https://github.com/spengliang/smoothvideo
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/spengliang/sudo
Diffusion Model Alignment Using Direct Preference Optimization
https://github.com/srayhit/TextToImageSynthesisUsingGAN
https://github.com/stability-ai/stablediffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/stanifrolov/spotdiffusion
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
https://github.com/stanislavfort/Direct_Ascent_Synthesis
Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models
https://github.com/stingram/CV_Image_Captioning
https://github.com/studian/CVND_P2_Image_Captioning
https://github.com/supreethub/Image-Captioning
https://github.com/suryar510/StackGAN
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/swookey-thinky/image_diffusion
PixArt-$α$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
https://github.com/syang-lab/Pathway_Autoregressive_Text2Image_Model
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
https://github.com/taki0112/AttnGAN-Tensorflow
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/taki0112/ControlGAN-Tensorflow
https://github.com/taoxugit/AttnGAN
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/teapearce/conditional_diffusion_mnist
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
https://github.com/tencent/hunyuandit
DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation
https://github.com/tencentqqgylab/ella
ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
https://github.com/teoaivalis/Search-Based_Data_Influence_Analysis
https://github.com/tgisaturday/taming-transformers-tpu
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.