5.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- SupélecFR485 papers
- University of Applied Sciences and Arts of Southern SwitzerlandCH485 papers
- New York UniversityUS52 papers
- Google (United States)US16 papers
- Princeton UniversityUS15 papers
- Courant Institute of Mathematical SciencesUS13 papers
- Meta (Israel)IL11 papers
- Massachusetts Institute of TechnologyUS10 papers
- Stanford UniversityUS8 papers
- University of California, BerkeleyUS8 papers
- Cornell UniversityUS7 papers
- Carnegie Mellon UniversityUS6 papers
11 papers · 2 filters
NAPA: Neural Art Human Pose Amplifier
Qingfu Wan, Oliver Lu
This is the project report for CSCI-GA.2271-001. We target human pose estimation in artistic images. For this goal, we design an end-to-end system that uses neural style transfer f…
Learned Equivariant Rendering without Transformation Supervision
Cinjon Resnick, Or Litany, Hugo Larochelle +2
We propose a self-supervised framework to learn scene representations from video that are automatically delineated into objects and background. Our method relies on moving objects…
Fine-Grained Re-Identification
Priyank Pathak
Research into the task of re-identification (ReID) is picking up momentum in computer vision for its many use cases and zero-shot learning nature. This paper proposes a computation…
Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability
Jason Phang, Jungkyu Park, Krzysztof J. Geras
Saliency maps that identify the most informative regions of an image for a classifier are valuable for model interpretability. A common approach to creating saliency maps involves…
Mind the Pad -- CNNs can Develop Blind Spots
Bilal Alsallakh, Narine Kokhlikyan, Vivek Miglani +2
We show how feature maps in convolutional networks are susceptible to spatial bias. Due to a combination of architectural choices, the activation at certain locations is systematic…
Encoding Robustness to Image Style via Adversarial Feature Perturbations
Manli Shu, Zuxuan Wu, Micah Goldblum +1
Adversarial training is the industry standard for producing models that are robust to small adversarial perturbations. However, machine learning practitioners need models that are…
ImagiFilter: A resource to enable the semi-automatic mining of images at scale
Houda Alberts, Iacer Calixto
Datasets (semi-)automatically collected from the web can easily scale to millions of entries, but a dataset's usefulness is directly related to how clean and high-quality its examp…
Robust Image Matching By Dynamic Feature Selection
Hao Huang, Jianchun Chen, Xiang Li +2
Estimating dense correspondences between images is a long-standing image under-standing task. Recent works introduce convolutional neural networks (CNNs) to extract high-level feat…
Using Human Psychophysics to Evaluate Generalization in Scene Text Recognition Models
Sahar Siddiqui, Elena Sizikova, Gemma Roig +2
Scene text recognition models have advanced greatly in recent years. Inspired by human reading we characterize two important scene text recognition models by measuring their domain…
Self-Supervised Learning of a Biologically-Inspired Visual Texture Model
Nikhil Parthasarathy, Eero P. Simoncelli
We develop a model for representing visual texture in a low-dimensional feature space, along with a novel self-supervised learning objective that is used to train it on an unlabele…
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
Abhinav Kumar, Tim K. Marks, Wenxuan Mou +6
Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted loca…