Text-to-Image Generation
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- 평가표
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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/ucsb-nlp-chang/diffusion_resampling
공식 코드https://github.com/univ-esuty/noisecollage
공식 코드NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging
https://github.com/usydnlp/VICTR
공식 코드VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Tasks
https://github.com/valeoai/halton-maskgit
공식 코드https://github.com/vancyland/prompt-agnostic-adversarial-perturbation-for-customized-diffusion-models.github.io
공식 코드Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
https://github.com/vancyland/vancyland.github.io-project-PAP
공식 코드Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
https://github.com/vectorspacelab/omnigen
공식 코드https://github.com/verazuo/prompt-stealing-attack
공식 코드Prompt Stealing Attacks Against Text-to-Image Generation Models
https://github.com/vfcosta/clip-gan-es
공식 코드Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution Strategies
https://github.com/vicgalle/stable-diffusion-aesthetic-gradients
공식 코드Personalizing Text-to-Image Generation via Aesthetic Gradients
https://github.com/videocrafter/talecrafter
공식 코드TaleCrafter: Interactive Story Visualization with Multiple Characters
https://github.com/vinid/text-to-image-bias
공식 코드Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
https://github.com/virajprabhu/lance
공식 코드LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
https://github.com/virobo-15/mgcc
공식 코드https://github.com/visual-words/visual-words
공식 코드https://github.com/vpulab/ovam
공식 코드Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models
https://github.com/vturrisi/disef
공식 코드Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
https://github.com/wangjiangshan0725/rf-solver-edit
공식 코드https://github.com/wangjiarui153/aigciqa2023
공식 코드https://github.com/wangkai930418/DPL
공식 코드Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
https://github.com/wangkaihong/hadm
공식 코드https://github.com/wangyuchi369/ladic
공식 코드https://github.com/wcshin-git/te-vqgan
공식 코드https://github.com/weihaox/TediGAN
공식 코드TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
https://github.com/weixi-feng/layoutgpt
공식 코드LayoutGPT: Compositional Visual Planning and Generation with Large Language Models
https://github.com/wfanyue/dpg-t2i-personalization
공식 코드Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement Learning
https://github.com/witcherofresearch/forgedit
공식 코드Forgedit: Text Guided Image Editing via Learning and Forgetting
https://github.com/wtliao/text2image
공식 코드https://github.com/wu-zongyu/posi
공식 코드Universal Prompt Optimizer for Safe Text-to-Image Generation
https://github.com/wutaiqiang/moslora
공식 코드https://github.com/wyf0912/aredit
공식 코드Training-Free Text-Guided Image Editing with Visual Autoregressive Model
https://github.com/wyuan1001/adavd
공식 코드Precise, Fast, and Low-cost Concept Erasure in Value Space: Orthogonal Complement Matters
https://github.com/xianfengwu01/lightgen
공식 코드https://github.com/xiexing0916/ARRA
공식 코드https://github.com/xudonmao/pairedit
공식 코드PairEdit: Learning Semantic Variations for Exemplar-based Image Editing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.