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Found 11 papers, 2 papers with code
VisualLens: Personalization through Visual History
We hypothesize that a user's visual history with images reflecting their daily life, offers valuable insights into their interests and preferences, and can be leveraged for personalization.
TLDR: Token-Level Detective Reward Model for Large Vision Language Models
Although reward models have been successful in improving multimodal large language models, the reward models themselves remain brutal and contain minimal information.
Pre-trained Large Language Models Use Fourier Features to Compute Addition
This paper shows that pre-trained LLMs add numbers using Fourier features -- dimensions in the hidden state that represent numbers via a set of features sparse in the frequency domain.
IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations
Current foundation models exhibit impressive capabilities when prompted either with text only or with both image and text inputs.
Simplicity Bias of Transformers to Learn Low Sensitivity Functions
Transformers achieve state-of-the-art accuracy and robustness across many tasks, but an understanding of the inductive biases that they have and how those biases are different from other neural network architectures remains elusive.
DeLLMa: Decision Making Under Uncertainty with Large Language Models
The potential of large language models (LLMs) as decision support tools is increasingly being explored in fields such as business, engineering, and medicine, which often face challenging tasks of decision-making under uncertainty.
DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback
Then, it uses two VLMs to select the best generation: a Visual Question Answering model that measures the alignment of generated images to the text, and another that measures the generation's aesthetic quality.
Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression
Transformers excel at in-context learning (ICL) -- learning from demonstrations without parameter updates -- but how they do so remains a mystery.
SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples
In this work, we propose Self-labeled Counterfactuals for Extrapolating to Negative Examples (SCENE), an automatic method for synthesizing training data that greatly improves models' ability to detect challenging negative examples.
Topological Regularization for Dense Prediction
Dense prediction tasks such as depth perception and semantic segmentation are important applications in computer vision that have a concrete topological description in terms of partitioning an image into connected components or estimating a function with a small number of local extrema corresponding to objects in the image.
Harnessing the Conditioning Sensorium for Improved Image Translation
Multi-modal domain translation typically refers to synthesizing a novel image that inherits certain localized attributes from a 'content' image (e. g. layout, semantics, or geometry), and inherits everything else (e. g. texture, lighting, sometimes even semantics) from a 'style' image.
