Scaling Vision Pre-Training to 4K Resolution
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911087729836032 |
|---|---|
| author | Shi, Baifeng Li, Boyi Cai, Han Lu, Yao Liu, Sifei Pavone, Marco Kautz, Jan Han, Song Darrell, Trevor Molchanov, Pavlo Yin, Hongxu |
| author_facet | Shi, Baifeng Li, Boyi Cai, Han Lu, Yao Liu, Sifei Pavone, Marco Kautz, Jan Han, Song Darrell, Trevor Molchanov, Pavlo Yin, Hongxu |
| contents | High-resolution perception of visual details is crucial for daily tasks. Current vision pre-training, however, is still limited to low resolutions (e.g., 378 x 378 pixels) due to the quadratic cost of processing larger images. We introduce PS3 that scales CLIP-style vision pre-training to 4K resolution with a near-constant cost. Instead of contrastive learning on global image representation, PS3 is pre-trained by selectively processing local regions and contrasting them with local detailed captions, enabling high-resolution representation learning with greatly reduced computational overhead. The pre-trained PS3 is able to both encode the global image at low resolution and selectively process local high-resolution regions based on their saliency or relevance to a text prompt. When applying PS3 to multi-modal LLM (MLLM), the resulting model, named VILA-HD, significantly improves high-resolution visual perception compared to baselines without high-resolution vision pre-training such as AnyRes and S^2 while using up to 4.3x fewer tokens. PS3 also unlocks appealing scaling properties of VILA-HD, including scaling up resolution for free and scaling up test-time compute for better performance. Compared to state of the arts, PS3 and VILA-HD outperform previous vision encoders (e.g., SigLIP2 and Perception Encoder) and MLLMs (e.g., NVILA and Qwen2.5-VL) respectively across multiple benchmarks and achieve better efficiency than latest token pruning approaches. Finally, we find current benchmarks do not require 4K-resolution perception, which motivates us to propose 4KPro, a new benchmark of image QA at 4K resolution, on which VILA-HD outperforms all previous MLLMs, including a 16.1% improvement over GPT-4o and a 7.5% improvement and 1.67x speedup over Qwen2.5-VL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19903 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scaling Vision Pre-Training to 4K Resolution Shi, Baifeng Li, Boyi Cai, Han Lu, Yao Liu, Sifei Pavone, Marco Kautz, Jan Han, Song Darrell, Trevor Molchanov, Pavlo Yin, Hongxu Computer Vision and Pattern Recognition High-resolution perception of visual details is crucial for daily tasks. Current vision pre-training, however, is still limited to low resolutions (e.g., 378 x 378 pixels) due to the quadratic cost of processing larger images. We introduce PS3 that scales CLIP-style vision pre-training to 4K resolution with a near-constant cost. Instead of contrastive learning on global image representation, PS3 is pre-trained by selectively processing local regions and contrasting them with local detailed captions, enabling high-resolution representation learning with greatly reduced computational overhead. The pre-trained PS3 is able to both encode the global image at low resolution and selectively process local high-resolution regions based on their saliency or relevance to a text prompt. When applying PS3 to multi-modal LLM (MLLM), the resulting model, named VILA-HD, significantly improves high-resolution visual perception compared to baselines without high-resolution vision pre-training such as AnyRes and S^2 while using up to 4.3x fewer tokens. PS3 also unlocks appealing scaling properties of VILA-HD, including scaling up resolution for free and scaling up test-time compute for better performance. Compared to state of the arts, PS3 and VILA-HD outperform previous vision encoders (e.g., SigLIP2 and Perception Encoder) and MLLMs (e.g., NVILA and Qwen2.5-VL) respectively across multiple benchmarks and achieve better efficiency than latest token pruning approaches. Finally, we find current benchmarks do not require 4K-resolution perception, which motivates us to propose 4KPro, a new benchmark of image QA at 4K resolution, on which VILA-HD outperforms all previous MLLMs, including a 16.1% improvement over GPT-4o and a 7.5% improvement and 1.67x speedup over Qwen2.5-VL. |
| title | Scaling Vision Pre-Training to 4K Resolution |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.19903 |