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Collections including paper arxiv:2404.16030

How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Paper • 2404.16821 • Published Apr 25 • 53
Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings

Paper • 2404.16820 • Published Apr 25 • 15
MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24 • 12

CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data

Paper • 2404.15653 • Published Apr 24 • 26
MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24 • 12

CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data

Paper • 2404.15653 • Published Apr 24 • 26
MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24 • 12
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

Paper • 2405.12130 • Published May 20 • 45
Reducing Transformer Key-Value Cache Size with Cross-Layer Attention

Paper • 2405.12981 • Published May 21 • 28

Papers - MoE - Routing - Softmax Normalization

MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24 • 12

Papers - Image - Datasets - LAION

MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24 • 12
Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Paper • 2406.06525 • Published Jun 10 • 64
Data curation via joint example selection further accelerates multimodal learning

Paper • 2406.17711 • Published Jun 25 • 3
Unveiling Encoder-Free Vision-Language Models

Paper • 2406.11832 • Published Jun 17 • 49

Papers - Image - Benchmarks - Clip

MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24 • 12

Papers - MoE - Image - MoDE

MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24 • 12

Papers - Image - Encoders - OpenClip

MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24 • 12
Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss

Paper • 2410.17243 • Published 20 days ago • 88

Papers - Image - Datasets - Flickr

MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24 • 12

Papers - MoE - Inference - Routing with Task Metadata

MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24 • 12

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