Search Results for author:
Found 12 papers, 7 papers with code
Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities
We introduce a simple method, whiteboard-of-thought prompting, to unlock the visual reasoning capabilities of multimodal large language models across modalities.
Generating Illustrated Instructions
We introduce the new task of generating Illustrated Instructions, i. e., visual instructions customized to a user's needs.
ViperGPT: Visual Inference via Python Execution for Reasoning
Answering visual queries is a complex task that requires both visual processing and reasoning.
Ranked #15 on
Zero-Shot Video Question Answer
on NExT-QA
Affective Faces for Goal-Driven Dyadic Communication
We introduce a video framework for modeling the association between verbal and non-verbal communication during dyadic conversation.
What You Can Reconstruct From a Shadow
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
Doubly Right Object Recognition: A Why Prompt for Visual Rationales
We propose a ``doubly right'' object recognition benchmark, where the metric requires the model to simultaneously produce both the right labels as well as the right rationales.
Task Bias in Vision-Language Models
Incidental supervision from language has become a popular approach for learning generic visual representations that can be prompted to perform many recognition tasks in computer vision.
Visual Classification via Description from Large Language Models
By basing decisions on these descriptors, we can provide additional cues that encourage using the features we want to be used.
Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse
Variational autoencoders (VAEs) suffer from posterior collapse, where the powerful neural networks used for modeling and inference optimize the objective without meaningfully using the latent representation.
Shadows Shed Light on 3D Objects
Experiments and visualizations show that the method is able to generate multiple possible solutions that are consistent with the observation of the shadow.
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
We present an algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature.
Ranked #10 on
Image Super-Resolution
on FFHQ 256 x 256 - 4x upscaling
(PSNR metric)
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution
This work identifies and addresses two important technical challenges in single-image super-resolution: (1) how to upsample an image without magnifying noise and (2) how to preserve large scale structure when upsampling.
