You Only Need Half: Boosting Data Augmentation by Using Partial Content
Fuente:
arXiv
Saved in:
| Main Authors: | Hu, Juntao, Wu, Yuan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generation
by: Wang, Zhengren, et al.
Published: (2026)
by: Wang, Zhengren, et al.
Published: (2026)
You Only Need Less Attention at Each Stage in Vision Transformers
by: Zhang, Shuoxi, et al.
Published: (2024)
by: Zhang, Shuoxi, et al.
Published: (2024)
Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling
by: Shao, Shitong, et al.
Published: (2023)
by: Shao, Shitong, et al.
Published: (2023)
You Only Erase Once: Erasing Anything without Bringing Unexpected Content
by: Zhu, Yixing, et al.
Published: (2026)
by: Zhu, Yixing, et al.
Published: (2026)
Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning
by: Lu, Haodong, et al.
Published: (2024)
by: Lu, Haodong, et al.
Published: (2024)
YoNoSplat: You Only Need One Model for Feedforward 3D Gaussian Splatting
by: Ye, Botao, et al.
Published: (2025)
by: Ye, Botao, et al.
Published: (2025)
Smart Feature is What You Need
by: Hu, Zhaoxin, et al.
Published: (2024)
by: Hu, Zhaoxin, et al.
Published: (2024)
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation
by: Noroozi, Mehdi, et al.
Published: (2024)
by: Noroozi, Mehdi, et al.
Published: (2024)
You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking
by: Wang, Xiyang, et al.
Published: (2023)
by: Wang, Xiyang, et al.
Published: (2023)
Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation
by: Hu, Xinrong, et al.
Published: (2025)
by: Hu, Xinrong, et al.
Published: (2025)
You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement
by: Yan, Qingsen, et al.
Published: (2024)
by: Yan, Qingsen, et al.
Published: (2024)
You Only Speak Once to See
by: Yang, Wenhao, et al.
Published: (2024)
by: Yang, Wenhao, et al.
Published: (2024)
You Only Look at Once for Real-time and Generic Multi-Task
by: Wang, Jiayuan, et al.
Published: (2023)
by: Wang, Jiayuan, et al.
Published: (2023)
OMGSR: You Only Need One Mid-timestep Guidance for Real-World Image Super-Resolution
by: Wu, Zhiqiang, et al.
Published: (2025)
by: Wu, Zhiqiang, et al.
Published: (2025)
LightStereo: Channel Boost Is All You Need for Efficient 2D Cost Aggregation
by: Guo, Xianda, et al.
Published: (2024)
by: Guo, Xianda, et al.
Published: (2024)
YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images
by: Liu, Chenguang, et al.
Published: (2024)
by: Liu, Chenguang, et al.
Published: (2024)
[MASK] is All You Need
by: Hu, Vincent Tao, et al.
Published: (2024)
by: Hu, Vincent Tao, et al.
Published: (2024)
ParameterNet: Parameters Are All You Need
by: Han, Kai, et al.
Published: (2023)
by: Han, Kai, et al.
Published: (2023)
Positive Label Is All You Need for Multi-Label Classification
by: Yuan, Zhixiang, et al.
Published: (2023)
by: Yuan, Zhixiang, et al.
Published: (2023)
You Only Train Once
by: Sakaridis, Christos
Published: (2025)
by: Sakaridis, Christos
Published: (2025)
You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning
by: Moutakanni, Théo, et al.
Published: (2024)
by: Moutakanni, Théo, et al.
Published: (2024)
SMFD-UNet: Semantic Face Mask Is The Only Thing You Need To Deblur Faces
by: Zami, Abduz
Published: (2026)
by: Zami, Abduz
Published: (2026)
Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need
by: Wang, Qiang, et al.
Published: (2025)
by: Wang, Qiang, et al.
Published: (2025)
You Sense Only Once Beneath: Ultra-Light Real-Time Underwater Object Detection
by: Dong, Jun, et al.
Published: (2025)
by: Dong, Jun, et al.
Published: (2025)
Scan-and-Print: Patch-level Data Summarization and Augmentation for Content-aware Layout Generation in Poster Design
by: Hsu, HsiaoYuan, et al.
Published: (2025)
by: Hsu, HsiaoYuan, et al.
Published: (2025)
Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation
by: Jin, Xiaoyu, et al.
Published: (2024)
by: Jin, Xiaoyu, et al.
Published: (2024)
You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image
by: Wang, Taoyue, et al.
Published: (2026)
by: Wang, Taoyue, et al.
Published: (2026)
You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs
by: Luo, Yihong, et al.
Published: (2024)
by: Luo, Yihong, et al.
Published: (2024)
AugmentGest: Can Random Data Cropping Augmentation Boost Gesture Recognition Performance?
by: Aboudeshish, Nada, et al.
Published: (2025)
by: Aboudeshish, Nada, et al.
Published: (2025)
Search is All You Need for Few-shot Anomaly Detection
by: Wang, Qishan, et al.
Published: (2025)
by: Wang, Qishan, et al.
Published: (2025)
Pairwise Comparisons Are All You Need
by: Chahine, Nicolas, et al.
Published: (2024)
by: Chahine, Nicolas, et al.
Published: (2024)
YOSE: You Only Select Essential Tokens for Efficient DiT-based Video Object Removal
by: Wu, Chenyang, et al.
Published: (2026)
by: Wu, Chenyang, et al.
Published: (2026)
You Only Look Bottom-Up for Monocular 3D Object Detection
by: Xiong, Kaixin, et al.
Published: (2024)
by: Xiong, Kaixin, et al.
Published: (2024)
Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data
by: Madadikhaljan, Mojgan, et al.
Published: (2026)
by: Madadikhaljan, Mojgan, et al.
Published: (2026)
FineVision: Open Data Is All You Need
by: Wiedmann, Luis, et al.
Published: (2025)
by: Wiedmann, Luis, et al.
Published: (2025)
Only-Style: Stylistic Consistency in Image Generation without Content Leakage
by: Aravanis, Tilemachos, et al.
Published: (2025)
by: Aravanis, Tilemachos, et al.
Published: (2025)
Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models
by: Li, Senmao, et al.
Published: (2024)
by: Li, Senmao, et al.
Published: (2024)
The Ultimate Combo: Boosting Adversarial Example Transferability by Composing Data Augmentations
by: Yun, Zebin, et al.
Published: (2023)
by: Yun, Zebin, et al.
Published: (2023)
Perceptual Inductive Bias Is What You Need Before Contrastive Learning
by: Li, Tianqin, et al.
Published: (2025)
by: Li, Tianqin, et al.
Published: (2025)
Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection
by: Zhang, Haichao, et al.
Published: (2023)
by: Zhang, Haichao, et al.
Published: (2023)
Similar Items
-
AgenticOCR: Parsing Only What You Need for Efficient Retrieval-Augmented Generation
by: Wang, Zhengren, et al.
Published: (2026) -
You Only Need Less Attention at Each Stage in Vision Transformers
by: Zhang, Shuoxi, et al.
Published: (2024) -
Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling
by: Shao, Shitong, et al.
Published: (2023) -
You Only Erase Once: Erasing Anything without Bringing Unexpected Content
by: Zhu, Yixing, et al.
Published: (2026) -
Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning
by: Lu, Haodong, et al.
Published: (2024)