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Found 13 papers, 7 papers with code
FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models
The pipeline is as follows: the image is passed to both a captioner model (i. e. BLIP) and a diffusion model (i. e., Stable Diffusion Model) to generate a text description and visual representation, respectively.
Functional Invariants to Watermark Large Transformers
The rapid growth of transformer-based models increases the concerns about their integrity and ownership insurance.
Gradpaint: Gradient-Guided Inpainting with Diffusion Models
For the specific task of image inpainting, the current guiding mechanism relies on copying-and-pasting the known regions from the input image at each denoising step.
Zero-shot spatial layout conditioning for text-to-image diffusion models
Large-scale text-to-image diffusion models have significantly improved the state of the art in generative image modelling and allow for an intuitive and powerful user interface to drive the image generation process.
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar
The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020, and the training of a dense prediction decoder against aerial lidar maps.
The Stable Signature: Rooting Watermarks in Latent Diffusion Models
For instance, it detects the origin of an image generated from a text prompt, then cropped to keep $10\%$ of the content, with $90$+$\%$ accuracy at a false positive rate below 10$^{-6}$.
DiffEdit: Diffusion-based semantic image editing with mask guidance
Semantic image editing is an extension of image generation, with the additional constraint that the generated image should be as similar as possible to a given input image.
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment
Vision and Language Pretraining has become the prevalent approach for tackling multimodal downstream tasks.
Transformer Decoders with MultiModal Regularization for Cross-Modal Food Retrieval
We propose a new retrieval framework, T-Food (Transformer Decoders with MultiModal Regularization for Cross-Modal Food Retrieval) that exploits the interaction between modalities in a novel regularization scheme, while using only unimodal encoders at test time for efficient retrieval.
Ranked #3 on
Cross-Modal Retrieval
on Recipe1M
FlexIT: Towards Flexible Semantic Image Translation
Via the latent space of an auto-encoder, we iteratively transform the input image toward the target point, ensuring coherence and quality with a variety of novel regularization terms.
FLAVA: A Foundational Language And Vision Alignment Model
State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety of downstream tasks.
Ranked #4 on
Image Retrieval
on MS COCO
Embedding Arithmetic of Multimodal Queries for Image Retrieval
We introduce the SIMAT dataset to evaluate the task of Image Retrieval with Multimodal queries.
DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion
Our strategy scales to a large number of tasks while having negligible memory and time overheads due to strict control of the parameters expansion.
Ranked #2 on
Incremental Learning
on ImageNet - 10 steps
