Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields
This paper proposes a technique that combines grid-based Neural Radiance Fields and mip-NeRF 360 to address aliasing and accelerate training. Error rates are 8%-76% lower than prior techniques and training is 22x faster than mip-NeRF 360.
Businesses that use Neural Radiance Field training for 3D modeling and rendering can implement this technique to improve accuracy and efficiency in their processes.
Expressive Text-to-Image Generation with Rich Text
This paper proposes using a rich-text editor to enable local style control and precise color rendering in text-to-image synthesis. It outperforms strong baselines with quantitative evaluations.
Businesses that use text-to-image synthesis can use a rich-text editor to improve the accuracy and customization options of their outputs.
Segment Everything Everywhere All at Once
This paper presents SEEM, a promptable, interactive model for segmenting everything everywhere all at once in an image. It introduces a versatile prompting engine, compositionality, interactivity, and semantic-awareness.
Businesses that use visual understanding, particularly in segmentation, can implement SEEM to improve human-AI interaction and accuracy in their processes.
CLIP's emergent ability for visual prompt engineering
Explores the use of visual prompt engineering for solving computer vision tasks beyond classification by editing in image space instead of text. Shows the power of this simple approach by achieving state-of-the-art in zero-shot referring expressions comprehension and strong performance in keypoint localization tasks.
Businesses can consider using CLIP for more than just classification tasks, by utilizing visual prompt engineering to enhance their computer vision capabilities.
SpectFormer: A novel transformer architecture for vision transformers
Proposes the novel SpectFormer architecture for vision transformers that combines spectral and multi-headed attention layers, resulting in improved performance on ImageNet-1K and other standard datasets. Shows consistent performance in downstream tasks such as object detection and instance segmentation on the MS-COCO dataset.
Businesses can consider using SpectFormer as a backbone for their vision transformer models to improve their performance on image recognition tasks.