https://github.com/aberdeenml/equicaps
공식 코드EquiCaps: Predictor-Free Pose-Aware Pre-Trained Capsule Networks
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/abertsch72/perspective-shifting
공식 코드He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues
https://github.com/abhi1kumar/SUP-NeRF
공식 코드https://github.com/abhibha1807/robustifying_visual_perception
공식 코드https://github.com/abhinavdhall/deepfake
공식 코드Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
https://github.com/abhinavkashyap/dct
공식 코드So Different Yet So Alike! Constrained Unsupervised Text Style Transfer
https://github.com/acappemin/video-to-audio-and-piano
공식 코드Towards Video to Piano Music Generation with Chain-of-Perform Support Benchmarks
https://github.com/activevisionlab/dfnet
공식 코드DFNet: Enhance Absolute Pose Regression with Direct Feature Matching
https://github.com/acuooooo/resmatch
공식 코드ResMatch: Residual Attention Learning for Local Feature Matching
https://github.com/adaptivemotorcontrollab/modelzoo-figures
공식 코드SuperAnimal pretrained pose estimation models for behavioral analysis
https://github.com/adjkamel/goa_bat
공식 코드3D Human Pose Estimation via Spatial Graph Order Attention and Temporal Body Aware Transformer
https://github.com/adlnlp/attention_vl
공식 코드https://github.com/adobe-research/domain-expansion
공식 코드HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and Manipulation
https://github.com/adwardlee/renderih
공식 코드RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation
https://github.com/aerialroboticsgroup/forest-vo
공식 코드ForestVO: Enhancing Visual Odometry in Forest Environments through ForestGlue
https://github.com/afeng-x/pixwizard
공식 코드PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions
https://github.com/afrozas/skip-thought-gan
공식 코드Generating Text through Adversarial Training using Skip-Thought Vectors
https://github.com/aghasemzadeh/openmpl
공식 코드https://github.com/agwabom/pema
공식 코드PEMA: An Offsite-Tunable Plug-in External Memory Adaptation for Language Models
https://github.com/ahmad-573/vurf
공식 코드VURF: A General-purpose Reasoning and Self-refinement Framework for Video Understanding
https://github.com/ahmedheakl/fish-counting
공식 코드Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding
https://github.com/ahundt/costar_dataset
공식 코드The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
https://github.com/ahundt/enas
공식 코드The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
https://github.com/ahupujr/ntire2025_eventdeblur_challenge
공식 코드NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results
https://github.com/ahupujr/refid
공식 코드A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild
https://github.com/ai-application-and-integration-lab/rdpn6d
공식 코드https://github.com/ai-forever/Kandinsky-2
공식 코드Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion
https://github.com/ai-forever/kandinsky-3
공식 코드https://github.com/ai-forever/ruscode
공식 코드RusCode: Russian Cultural Code Benchmark for Text-to-Image Generation
https://github.com/ai-med/stablepose
공식 코드Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image Generation
https://github.com/ai-s2-lab/m2s-add
공식 코드Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion
https://github.com/aidc-ai/awesome-unified-multimodal-models
공식 코드Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities
https://github.com/aidc-ai/ovis-u1
공식 코드https://github.com/aigagror/wasserstein-style-transfer
공식 코드https://github.com/aiiu-lab/DemonSampling
공식 코드Training-free Diffusion Model Alignment with Sampling Demons
https://github.com/aik2mlj/polyffusion
공식 코드Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.