We introduce a new approach, Generative mOdel finetun-ing with Reward-driven Sample selection (GORS), to boost the compositional text-to-image generation abilities of pretrained text-to-image models.

The ability to collect a large dataset of human preferences from text-to-image users is usually limited to companies, making such datasets inaccessible to the public. To address this issue, we create a web app that enables text-to-image users to generate images and specify their preferences. Using this web app we build Pick-a-Pic, a large, open dataset of text-to-image prompts and real users'

The base model takes surround-view images of the ego-vehicle as input. As shown in Figure 1, we provide further visual comparisons of HD map vectorization results. The results reaffirm the necessity of a rasterization perspective in map vectorization. Figure 1 presents more visualization of MapVR's HD map construction results. As discussed in Section 3, the Chamfer-distance-based metric struggles to offer a fair evaluation for such scenarios.

MapVR (Map V ectorization via Rasterization), a novel framework that applies differentiable rasterization to vectorized outputs and then performs precise and geometry-aware supervision on rasterized HD maps.

Gaussian blurring, often used as a denoising technique for images, employs a Gaussian distribution to establish a convolution matrix that's applied to the original image. The fundamental idea involves substituting the noisy pixel with a weighted average of surrounding pixel values.

In this setting, neural operators trained to minimize squared error losses, while capable of accurate short-term forecasts, often fail to reproduce statistical or structural properties of the dynamics over longer time horizons and can yield degenerate results.

We study cultural and socioeconomic diversity in contrastive vision-language models (VLMs). Using a broad range of benchmark datasets and evaluation metrics, we bring to attention several important findings.