https://github.com/neuroethology/TREBA
공식 코드The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Style Transfer is a technique in computer vision and graphics that involves generating a new image by combining the content of one image with the style of another image. The goal of style transfer is to create an image that preserves the content of the original image while applying the visual style of another image.
<span style="color:grey; opacity: 0.6">( Image credit: A Neural Algorithm of Artistic Style )</span>
The "T" horizontal strip can mimic the back of a sofa with a delicate cushion or details of the uphols or appliances with the color button.
The "T" vertical strip can show a feet or arm of the sofa, shiny, yet firm.
Put "P" next to "T", your curve to delicately with the top "T." It is intertwined. The circular part of "P" can show a cushion or a curved chair and synchronize the subject of furniture.
Make sure "P" is visually relying on "T", which reflects the relationship of cohesion and balance.
"B" can be aligned as a pair of cushions or a modern chair, with mild curves with glossy and modern aesthetics.
"I" can be a symbol of a shiny furniture or a vertical light bar and completes the shapes without overburdess them.
Color palette 4:
Includes soft soil colors such as beige, top and gray shades, along with silent or silver gold tips to touch elegance.
Consider a slope effect to enhance modernity, to keep colors elegant and complex.
Use the overlap or intertwined edges that the letters meet for the symbol of unity.
The plan should allow viewers to distinguish each letter while feeling part of the same "structure".
Use delicate geometric patterns or textures that mimic fabrics or furniture materials such as wood seeds or woven fibers.
These patterns must remain minimalist and focus on highlighting the logo, while maintaining communication.
While it deals with the subject of furniture and design, this concept conveys modernity, creativity and professional. If you like, I can create a draft design for better visualization.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
Learning from Multi-domain Artistic Images for Arbitrary Style Transfer
In-Style: Bridging Text and Uncurated Videos with Style Transfer for Text-Video Retrieval
Large-scale Hierarchical Alignment for Data-driven Text Rewriting
Style Transfer by Relaxed Optimal Transport and Self-Similarity
Causal Inference via Style Transfer for Out-of-distribution Generalisation
MSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style Transfer
D2Styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods
TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps
UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
SPAC-Net: Synthetic Pose-aware Animal ControlNet for Enhanced Pose Estimation
Prior-Aware Synthetic Data to the Rescue: Animal Pose Estimation with Very Limited Real Data
FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer
Multilingual Text Style Transfer: Datasets & Models for Indian Languages
CLIPstyler: Image Style Transfer with a Single Text Condition
Rethinking and Improving the Robustness of Image Style Transfer
ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows
Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.