The ViT model we use in this work is based on a standard Vision Transformer [7] model scaled to577 nearly 3 billion parameters, using a patch size of 14, 16 heads, 64 blocks, an MLP dimension of 8192578 and a hidden dimension of 2048. The model is defined and trained in Lingvo [32]; we additionally579 employ GSPMD [41] for training. The model is pre-trained on JFT-3B [35] using training settings580 that optimize for performance on JFT-3B rather than for fine-tuning on ImageNet; notably, we do not581 use the training recipe that helps few-shot transfer performance [44]. BReview tools586 We include screenshots of the reviewing tools we built to analyze model mistakes. Figure 3 shows587 the UI for reviewing model predictions and Figure 4 shows the UI that displays the labeling guide588 and slide bar to browse images for a particular class.

A batch size of 2048 is used during training with a learning rate of 1e-4. Both training and rendering were conducted using A WS. A.2 PixelNeRF We used a constant learning rate of 1e-4. To train PixelNeRF on Objaverse-XL we render the meshes in Blender. Each model is normalize to a bounding cube. We believe that models such as Zero123-XL, and those trained on Objaverse-XL, will enhance the ease of 3D content creation, enabling broader accessibility for individuals and businesses to participate.

There are two operations in our data augmentation pipeline that changes the scale or layout of the image, i.e ., random resized crop and random horizontal flip. This is followed by a resize operation to recover the intersect part to the original size ( e.g ., RoIAlign to recover the original spatial layout. The total stride is 16 (FCN-16s [20]). Intuitively, each prototype can be viewed as the cluster center of a semantic class. During inference, we only take the teacher model parameterized by ξ .

To handle this setting, we use the encoderΘofthe autoencoder trained onAnimalsalso forΦ,butalways apply a spatial deformation to its inputs.

Large language models have recently shown a remarkable ability for few-shot learning, including patterns of algorithmic nature. However, it is still an open question to determine what kind of patterns these models can capture and how manyexamples theyneedintheirprompts.