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Collections including paper arxiv:2404.03653

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6 • 21
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6 • 9
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7 • 31
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7 • 19

Improving Visual Text Alignment

ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Paper • 2403.05135 • Published Mar 8 • 39
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching

Paper • 2404.03653 • Published Apr 4 • 28

Image Generation

about 10 hours ago

EdgeFusion: On-Device Text-to-Image Generation

Paper • 2404.11925 • Published Apr 18 • 19
Dynamic Typography: Bringing Words to Life

Paper • 2404.11614 • Published Apr 17 • 40
ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback

Paper • 2404.07987 • Published Apr 11 • 46
Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models

Paper • 2404.07724 • Published Apr 11 • 10

CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching

Paper • 2404.03653 • Published Apr 4 • 28

Vision and language

No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

Paper • 2404.04125 • Published Apr 4 • 27
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching

Paper • 2404.03653 • Published Apr 4 • 28
Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models

Paper • 2404.02747 • Published Apr 3 • 11
3D Congealing: 3D-Aware Image Alignment in the Wild

Paper • 2404.02125 • Published Apr 2 • 6

CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching

Paper • 2404.03653 • Published Apr 4 • 28

CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching

Paper • 2404.03653 • Published Apr 4 • 28

CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching

Paper • 2404.03653 • Published Apr 4 • 28
LVLM-Intrepret: An Interpretability Tool for Large Vision-Language Models

Paper • 2404.03118 • Published Apr 3 • 19

Papers - Image - SDXL

On the Scalability of Diffusion-based Text-to-Image Generation

Paper • 2404.02883 • Published Apr 3 • 17
InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation

Paper • 2404.02733 • Published Apr 3 • 19
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching

Paper • 2404.03653 • Published Apr 4 • 28
ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback

Paper • 2404.07987 • Published Apr 11 • 46

U-Net: Convolutional Networks for Biomedical Image Segmentation

Paper • 1505.04597 • Published May 18, 2015 • 6
Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Images

Paper • 2310.16186 • Published Oct 24, 2023 • 2
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes

Paper • 1709.07330 • Published Sep 21, 2017 • 2
Deep LOGISMOS: Deep Learning Graph-based 3D Segmentation of Pancreatic Tumors on CT scans

Paper • 1801.08599 • Published Jan 25, 2018 • 2

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