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Found 11 papers, 0 papers with code
Mixed-Query Transformer: A Unified Image Segmentation Architecture
Existing unified image segmentation models either employ a unified architecture across multiple tasks but use separate weights tailored to each dataset, or apply a single set of weights to multiple datasets but are limited to a single task.
On the Scalability of Diffusion-based Text-to-Image Generation
On the data scaling side, we show the quality and diversity of the training set matters more than simply dataset size.
CPR: Retrieval Augmented Generation for Copyright Protection
To reduce risks of leaking private information contained in the retrieved set, we introduce Copy-Protected generation with Retrieval (CPR), a new method for RAG with strong copyright protection guarantees in a mixed-private setting for diffusion models. CPR allows to condition the output of diffusion models on a set of retrieved images, while also guaranteeing that unique identifiable information about those example is not exposed in the generated outputs.
Multi-Modal Hallucination Control by Visual Information Grounding
In particular, we show that as more tokens are generated, the reliance on the visual prompt decreases, and this behavior strongly correlates with the emergence of hallucinations.
Fast Sparse View Guided NeRF Update for Object Reconfigurations
Neural Radiance Field (NeRF), as an implicit 3D scene representation, lacks inherent ability to accommodate changes made to the initial static scene.
A Quantitative Evaluation of Score Distillation Sampling Based Text-to-3D
However, the SDS method is also the source of several artifacts, such as the Janus problem, the misalignment between the text prompt and the generated 3D model, and 3D model inaccuracies.
Training Data Protection with Compositional Diffusion Models
We introduce Compartmentalized Diffusion Models (CDM), a method to train different diffusion models (or prompts) on distinct data sources and arbitrarily compose them at inference time.
Towards Visual Foundational Models of Physical Scenes
We describe a first step towards learning general-purpose visual representations of physical scenes using only image prediction as a training criterion.
SAFE: Machine Unlearning With Shard Graphs
We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large models on a diverse collection of data while minimizing the expected cost to remove the influence of training samples from the trained model.
Learning Expressive Prompting With Residuals for Vision Transformers
Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model.
A Meta-Learning Approach to Predicting Performance and Data Requirements
The PPL improves the performance estimation on average by 37% across 16 classification and 33% across 10 detection datasets, compared to the power law.
