164 citations · 1.3k across the 73 of their papers we have counts for
15 papers · 1 filter
Vista4D: Video Reshooting with 4D Point Clouds
Kuan Heng Lin, Zhizheng Liu, Pablo Salamanca +9
We present Vista4D, a robust and flexible video reshooting framework that grounds the input video and target cameras in a 4D point cloud. Specifically, given an input video, our me…
FineGRAIN: Evaluating Failure Modes of Text-to-Image Models with Vision Language Model Judges
Kevin David Hayes, Micah Goldblum, Vikash Sehwag +3
Text-to-image (T2I) models are capable of generating visually impressive images, yet they often fail to accurately capture specific attributes in user prompts, such as the correct…
Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning
Ang Li, Charles Wang, Deqing Fu +9
Humans often use visual aids, for example diagrams or sketches, when solving complex problems. Training multimodal models to do the same, known as Visual Chain of Thought (Visual C…
Measuring Style Similarity in Diffusion Models
Gowthami Somepalli, Anubhav Gupta, Kamal Gupta +5
Generative models are now widely used by graphic designers and artists. Prior works have shown that these models remember and often replicate content from their training data durin…
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
Micah Goldblum, Hossein Souri, Renkun Ni +10
Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an…
A Simple and Efficient Baseline for Data Attribution on Images
Vasu Singla, Pedro Sandoval-Segura, Micah Goldblum +2
Data attribution methods play a crucial role in understanding machine learning models, providing insight into which training data points are most responsible for model outputs duri…
Seeing in Words: Learning to Classify through Language Bottlenecks
Khalid Saifullah, Yuxin Wen, Jonas Geiping +2
Neural networks for computer vision extract uninterpretable features despite achieving high accuracy on benchmarks. In contrast, humans can explain their predictions using succinct…
Universal Guidance for Diffusion Models
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild +4
Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining. In this work…
What do Vision Transformers Learn? A Visual Exploration
Amin Ghiasi, Hamid Kazemi, Eitan Borgnia +5
Vision transformers (ViTs) are quickly becoming the de-facto architecture for computer vision, yet we understand very little about why they work and what they learn. While existing…
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Arpit Bansal, Eitan Borgnia, Hong-Min Chu +6
Standard diffusion models involve an image transform -- adding Gaussian noise -- and an image restoration operator that inverts this degradation. We observe that the generative beh…
A Deep Dive into Dataset Imbalance and Bias in Face Identification
Valeriia Cherepanova, Steven Reich, Samuel Dooley +3
As the deployment of automated face recognition (FR) systems proliferates, bias in these systems is not just an academic question, but a matter of public concern. Media portrayals…
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
Amin Ghiasi, Hamid Kazemi, Steven Reich +3
Existing techniques for model inversion typically rely on hard-to-tune regularizers, such as total variation or feature regularization, which must be individually calibrated for ea…
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability
Roman Levin, Manli Shu, Eitan Borgnia +3
Conventional saliency maps highlight input features to which neural network predictions are highly sensitive. We take a different approach to saliency, in which we identify and ana…
The Intrinsic Dimension of Images and Its Impact on Learning
Phillip Pope, Chen Zhu, Ahmed Abdelkader +2
It is widely believed that natural image data exhibits low-dimensional structure despite the high dimensionality of conventional pixel representations. This idea underlies a common…
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition
Valeriia Cherepanova, Micah Goldblum, Harrison Foley +4
Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. These…