https://github.com/soon-yau/visconet
공식 코드ViscoNet: Bridging and Harmonizing Visual and Textual Conditioning for ControlNet
Papers with Code 아카이브
Visual Prompting
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Visual Prompting is the task of streamlining computer vision processes by harnessing the power of prompts, inspired by the breakthroughs of text prompting in NLP. This innovative approach involves using a few visual prompts to swiftly convert an unlabeled dataset into a deployed model, significantly reducing development time for both individual projects and enterprise solutions.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/thaoshibe/visii
공식 코드Visual Instruction Inversion: Image Editing via Visual Prompting
https://github.com/tmlr-group/wca
공식 코드Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models
https://github.com/ucsc-vlaa/evp
공식 코드https://github.com/unites-lab/vpns
공식 코드Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective
https://github.com/ux-decoder/dinov
공식 코드https://github.com/wivizhang/earthmarker
공식 코드EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote Sensing
https://github.com/xiaohao-xu/ambiguity-in-space
공식 코드Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth Ambiguity
https://github.com/xr-lee/neural-symbolic
공식 코드Chameleon: Fast-slow Neuro-symbolic Lane Topology Extraction
https://github.com/ycyinchao/rdvp-msd
공식 코드https://github.com/ylingfeng/FGVP
공식 코드https://github.com/yu-rp/apiprompting
공식 코드Attention Prompting on Image for Large Vision-Language Models
https://github.com/zhoujiahuan1991/aaai2025-svp
공식 코드https://github.com/zhoujiahuan1991/icml2025-tcpa
공식 코드Token Coordinated Prompt Attention is Needed for Visual Prompting
https://github.com/zhoujiahuan1991/icml2025-vgp
공식 코드https://github.com/zybeich/DPCore
공식 코드Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time Adaptation
https://github.com/zybeich/ot-vp
공식 코드OT-VP: Optimal Transport-guided Visual Prompting for Test-Time Adaptation
https://github.com/zycheiheihei/transferable-visual-prompting
공식 코드Exploring the Transferability of Visual Prompting for Multimodal Large Language Models
https://github.com/zyxElsa/IP-Prompter
공식 코드IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/sam
https://github.com/HibaKob/MicroBundlePillarTrack
https://github.com/IDEA-Research/Grounded-Segment-Anything
https://github.com/MS-P3/code7/tree/main/vipllava
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
https://github.com/MindCode-4/code-1/tree/main/vipllava
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
https://github.com/MindCode-4/code-5/tree/main/vipllava
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
https://github.com/OthersideAI/self-operating-computer
Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
https://github.com/PaddlePaddle/PaddleSeg
https://github.com/ZE-WEN/DendriteSAM
https://github.com/adverML/vp-for-adversarial-robustness
https://github.com/aim-uofa/matcher
https://github.com/arnab39/equiadapt
https://github.com/camenduru/grounded-segment-anything-colab
https://github.com/chwilms/sos
https://github.com/computational-cell-analytics/medico-sam
https://github.com/computational-cell-analytics/micro-sam
https://github.com/computational-cell-analytics/patho-sam
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.