Search Results for author:
Found 7 papers, 3 papers with code
ParGANDA: Making Synthetic Pedestrians A Reality For Object Detection
Object detection is the key technique to a number of Computer Vision applications, but it often requires large amounts of annotated data to achieve decent results.
Generative Adversarial Network
Image-to-Image Translation
+4
AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts
Instead of compressing multiple tasks' knowledge into a single model, MoE separates the parameter space and only utilizes the relevant model pieces given task type and its input, which provides stabilized MTL training and ultra-efficient inference.
Efficient Image Representation Learning with Federated Sampled Softmax
This work creates a possibility for efficiently learning image representations on decentralized data with a large number of classes under the federated setting.
A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
In this paper, we comprehensively study three architecture design choices on ViT -- spatial reduction, doubled channels, and multiscale features -- and demonstrate that a vanilla ViT architecture can fulfill this goal without handcrafting multiscale features, maintaining the original ViT design philosophy.
Sky Optimization: Semantically aware image processing of skies in low-light photography
The sky is a major component of the appearance of a photograph, and its color and tone can strongly influence the mood of a picture.
What's in a Name? First Names as Facial Attributes
We show that describing people in terms of similarity to a vector of possible first names is a powerful description of facial appearance that can be used for face naming and building facial attribute classifiers.
Describing Clothing by Semantic Attributes
Describing clothing appearance with semantic attributes is an appealing technique for many important applications.
