SlowGuess/NeurIPS_2023_OCR huggingface dataset:
As demonstrated in qualitative results, our CONPREDIFF is able to synthesize realistic diverse, context-coherent images. However, using FID to estimate the sample quality is not always consistent with human judgment. Therefore, we follow the protocol of previous works [104, 68, 62], and conduct systematic human evaluations to better assess the generation capacities of our CONPREDIFF from the aspects of image photorealism and image-text alignment. We conduct side-by-side human evaluations, in which well-trained users are presented with two generated images for the same prompt and need to choose which image is of higher quality and more realistic (image photorealism) and which image better matches the input prompt (image-text alignment). For evaluating the coherence of local context, we propose a new evaluation protocol, in which users are presented with 1000 pairs of images and must choose which image better preserves local pixel/semantic continuity. The evaluation results are in Tab. 4, CONPREDIFF performs better in pairwise comparisons against both Improved VQ-Diffusion and Imagen. We find that CONPREDIFF is preferred in terms of all three evaluations, and CONPREDIFF is strongly preferred regarding context coherence, demonstrating that preserving local neighborhood context advances sample quality and semantic alignment.
Table 4: Human evaluation comparing CONPREDIFF to Improved VQ-Diffusion and Imagen.
Improved VQ-Diffusion	Imagen
Image Photorealism	72%	65%
Image-Text Alignment	68%	63%
Context Coherence	84%	78%