Scaling Vision Pre-Training to 4K Resolution
Authors: name: Baifeng Shi; name: Boyi Li; name: Han Cai; name: Yao Lu; name: Sifei Liu; name: Marco Pavone; name: Jan Kautz; name: Song Han; name: Trevor Darrell; name: Pavlo Molchanov; name: Hongxu Yin
Published: 2025-03-25 17:58:37

Abstract: 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, VILA-HD outperforms previous MLLMs such as NVILA and Qwen2-VL
across multiple benchmarks and achieves 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 14.5% improvement over GPT-4o, and a 3.2% improvement and 2.96x
speedup over Qwen2-VL.