Search Results for author:
Found 13 papers, 5 papers with code
[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
The big changes for this year's competition are as follows: First, we replace the loose track with a paper track, which allows (for example) non-model-based submissions, novel cognitively-inspired benchmarks, or analysis techniques.
Improving Text-to-Image Consistency via Automatic Prompt Optimization
In this paper, we address these challenges and introduce a T2I optimization-by-prompting framework, OPT2I, which leverages a large language model (LLM) to improve prompt-image consistency in T2I models.
Leveraging Diffusion Perturbations for Measuring Fairness in Computer Vision
In this work, we demonstrate that diffusion models can be leveraged to create such a dataset.
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs.
Ranked #2 on
Text-to-Image Generation
on MS COCO
FACET: Fairness in Computer Vision Evaluation Benchmark
We present a new benchmark named FACET (FAirness in Computer Vision EvaluaTion), a large, publicly available evaluation set of 32k images for some of the most common vision tasks - image classification, object detection and segmentation.
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
The unprecedented photorealistic results achieved by recent text-to-image generative systems and their increasing use as plug-and-play content creation solutions make it crucial to understand their potential biases.
Towards Reliable Assessments of Demographic Disparities in Multi-Label Image Classifiers
These metrics successfully incentivized performance improvements on person-centric tasks such as face analysis and are used to understand risks of modern models.
Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities
With these capabilities in mind, we ask: Do vision-language models exhibit gender bias when performing zero-shot image classification, object detection and semantic segmentation?
Perturbation Augmentation for Fairer NLP
Unwanted and often harmful social biases are becoming ever more salient in NLP research, affecting both models and datasets.
Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality
We present a novel task and dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning, which we call Winoground.
Ranked #41 on
Visual Reasoning
on Winoground
CM3: A Causal Masked Multimodal Model of the Internet
We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents that can contain both text and image tokens.
Learning a natural-language to LTL executable semantic parser for grounded robotics
We take a step toward robots that can do the same by training a grounded semantic parser, which discovers latent linguistic representations that can be used for the execution of natural-language commands.
Measuring Social Biases in Grounded Vision and Language Embeddings
We generalize the notion of social biases from language embeddings to grounded vision and language embeddings.
