Text-to-Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/hangligit/InterpretDiffusion
Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation
https://github.com/hansam95/nmg
https://github.com/hanzhanggit/StackGAN-Pytorch
https://github.com/hanzhanggit/StackGAN-inception-model
https://github.com/harvard-visionlab/lrm-steering
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/hashi0203/image-captioning
https://github.com/hg1722/fashionista
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/hiarsal/dae-gan
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/huggingface/diffusers
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
https://github.com/huggingface/open-muse
Muse: Text-To-Image Generation via Masked Generative Transformers
https://github.com/hx19940102/Image-Captioning
https://github.com/hxixixh/adaflow
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
https://github.com/hyn2028/llm-cxr
https://github.com/idonahum/photoVerse
PhotoVerse: Tuning-Free Image Customization with Text-to-Image Diffusion Models
https://github.com/ignoww/ZOODiP
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
https://github.com/iigroup/mm-celeba-hq-dataset
Conditional Image Generation and Manipulation for User-Specified Content
https://github.com/illume-unified-mllm/ILLUME_plus
ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement
https://github.com/itsayushthada/Text-To-Image
https://github.com/jacklishufan/Reflect-DiT
https://github.com/jackonelli/mcmc_corr_score_diffusion
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
https://github.com/jam-ing/DALLE-pytorch
https://github.com/jay-z007/Text-to-Image-Synthesis
https://github.com/jazzsaxmafia/show_and_tell.tensorflow
https://github.com/jdrego/EEE511ANC-Text2ImageGAN
https://github.com/jelifysh/Image-Captioning
https://github.com/jielin-qiu/mm_robustness
Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift
https://github.com/jishubasak/Punny-Caption--Exploring-Image-Captioning
https://github.com/joanrod/figure-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/joanrod/ocr-vqgan
https://github.com/joh-fischer/PlantLDM
https://github.com/juletx/image-caption-generation
https://github.com/julianjuaner/rival
Real-World Image Variation by Aligning Diffusion Inversion Chain
https://github.com/kakaobrain/coyo-dataset
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/kauravin/Neural-Visual-Image-Caption-Generation-using-RNNs-and-LSTMs
https://github.com/kenkai21/Image_Captioning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.