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Found 8 papers, 5 papers with code
Evaluating Text-to-Visual Generation with Image-to-Text Generation
For instance, the widely-used CLIPScore measures the alignment between a (generated) image and text prompt, but it fails to produce reliable scores for complex prompts involving compositions of objects, attributes, and relations.
DIME-FM: DIstilling Multimodal and Efficient Foundation Models
We transfer the knowledge from the pre-trained CLIP-ViTL/14 model to a ViT-B/32 model, with only 40M public images and 28. 4M unpaired public sentences.
DIME-FM : DIstilling Multimodal and Efficient Foundation Models
In this paper, we introduce a new distillation mechanism (DIME-FM) that allows us to transfer the knowledge contained in large VLFMs to smaller, customized foundation models using a relatively small amount of inexpensive, unpaired images and sentences.
Real-time Localized Photorealistic Video Style Transfer
We present a novel algorithm for transferring artistic styles of semantically meaningful local regions of an image onto local regions of a target video while preserving its photorealism.
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera.
Learning to Approximate a Bregman Divergence
Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergence, and arise throughout many areas of machine learning.
Moment Matching for Multi-Source Domain Adaptation
Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain.
W-Net: A Deep Model for Fully Unsupervised Image Segmentation
While significant attention has been recently focused on designing supervised deep semantic segmentation algorithms for vision tasks, there are many domains in which sufficient supervised pixel-level labels are difficult to obtain.
