https://github.com/fdistinctions/ijcai18
공식 코드Empirical Analysis of Foundational Distinctions in Linked Open Data
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/flytxtds/scene-recognition
공식 코드Scene Recognition by Combining Local and Global Image Descriptors
https://github.com/fudan-generative-vision/champ
공식 코드Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
https://github.com/fudan-generative-vision/hallo2
공식 코드Hallo2: Long-Duration and High-Resolution Audio-Driven Portrait Image Animation
https://github.com/fudan-generative-vision/hallo3
공식 코드Hallo3: Highly Dynamic and Realistic Portrait Image Animation with Video Diffusion Transformer
https://github.com/ganggit/deepCRFs
공식 코드https://github.com/ganglii/devsafety
공식 코드A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety
https://github.com/gulucaptain/dynamictrl
공식 코드https://github.com/guoyww/animatediff
공식 코드AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
https://github.com/hashtag00002/tt-df
공식 코드TT-DF: A Large-Scale Diffusion-Based Dataset and Benchmark for Human Body Forgery Detection
https://github.com/huangjiadidi/DeepFakeMnist
공식 코드https://github.com/hydropml/disasternets
공식 코드DisasterNets: Embedding Machine Learning in Disaster Mapping
https://github.com/itsyoavshalev/Image-Animation-with-Perturbed-Masks
공식 코드https://github.com/jaychempan/pir-clip
공식 코드PIR: Remote Sensing Image-Text Retrieval with Prior Instruction Representation Learning
https://github.com/jdh-algo/JoyVASA
공식 코드https://github.com/jialetao/motrans
공식 코드https://github.com/laura-wang/video_repres_sts
공식 코드Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics
https://github.com/lihxxx/dispose
공식 코드DisPose: Disentangling Pose Guidance for Controllable Human Image Animation
https://github.com/martianxiu/als_pretraining
공식 코드https://github.com/mattdl/DUA
공식 코드Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem
https://github.com/maxin-cn/Cinemo
공식 코드Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models
https://github.com/mayuelala/followyourclick
공식 코드Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts
https://github.com/mcg-nju/comae
공식 코드CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets
https://github.com/myniuuu/mofa-video
공식 코드https://github.com/open-mmlab/PIA
공식 코드PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models
https://github.com/or-toledano/animation-with-keypoint-mask
공식 코드https://github.com/ownstyledu/Translate-to-Recognize-Networks
공식 코드https://github.com/pjlab-adg/gpt4v-ad-exploration
공식 코드On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving
https://github.com/princetonvisualai/imagenet-face-obfuscation
공식 코드https://github.com/r-seny/narrativexl
공식 코드NarrativeXL: A Large-scale Dataset For Long-Term Memory Models
https://github.com/rkwitt/GuidedAugmentation
공식 코드https://github.com/salesforce/lavis
공식 코드Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions
https://github.com/shramanpramanick/transformer_based_geo-localization
공식 코드Where in the World is this Image? Transformer-based Geo-localization in the Wild
https://github.com/silvia1993/multi-modal_rgb-d_scene_recognition_across_domains
공식 코드https://github.com/songxinhang/D-CNN
공식 코드Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs
https://github.com/text2cinemagraph/text2cinemagraph
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.