49 citations · 64 across the 5 of their papers we have counts for
6 papers
Feedback-Driven Vision-Language Alignment with Minimal Human Supervision
Giorgio Giannone, Ruoteng Li, Qianli Feng +3
Vision-language models (VLMs) have demonstrated remarkable potential in integrating visual and linguistic information, but their performance is often constrained by the need for ex…
Near Perfect GAN Inversion
Qianli Feng, Viraj Shah, Raghudeep Gadde +2
To edit a real photo using Generative Adversarial Networks (GANs), we need a GAN inversion algorithm to identify the latent vector that perfectly reproduces it. Unfortunately, wher…
When do GANs replicate? On the choice of dataset size
Qianli Feng, Chenqi Guo, Fabian Benitez-Quiroz +1
Do GANs replicate training images? Previous studies have shown that GANs do not seem to replicate training data without significant change in the training procedure. This leads to…
Diamond in the rough: Improving image realism by traversing the GAN latent space
Jeffrey Wen, Fabian Benitez-Quiroz, Qianli Feng +1
In just a few years, the photo-realism of images synthesized by Generative Adversarial Networks (GANs) has gone from somewhat reasonable to almost perfect largely by increasing the…
Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent
Stuart Synakowski, Qianli Feng, Aleix Martinez
Computer vision algorithms performance are near or superior to humans in the visual problems including object recognition (especially those of fine-grained categories), segmentatio…
EmotioNet Challenge: Recognition of facial expressions of emotion in the wild
C. Fabian Benitez-Quiroz, Ramprakash Srinivasan, Qianli Feng +2
This paper details the methodology and results of the EmotioNet challenge. This challenge is the first to test the ability of computer vision algorithms in the automatic analysis o…