26 citations · 54 across the 9 of their papers we have counts for
7 papers · 1 filter
Foveated Reasoning: Stateful, Action-based Visual Focusing for Vision-Language Models
Juhong Min, Lazar Valkov, Vitali Petsiuk +2
Vision-language models benefit from high-resolution images, but the increase in visual-token count incurs high compute overhead. Humans resolve this tension via foveation: a coarse…
GoldiCLIP: The Goldilocks Approach for Balancing Explicit Supervision for Language-Image Pretraining
Deen Dayal Mohan, Hossein Souri, Vitali Petsiuk +4
Until recently, the success of large-scale vision-language models (VLMs) has primarily relied on billion-sample datasets, posing a significant barrier to progress. Latest works hav…
ARGENT: Adaptive Hierarchical Image-Text Representations
Chuong Huynh, Hossein Souri, Abhinav Kumar +3
Large-scale Vision-Language Models (VLMs) such as CLIP learn powerful semantic representations but operate in Euclidean space, which fails to capture the inherent hierarchical stru…
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 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…
Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition
Prithviraj Dhar, Joshua Gleason, Hossein Souri +2
State-of-the-art deep networks implicitly encode gender information while being trained for face recognition. Gender is often viewed as an important attribute with respect to ident…