13 citations · 33 across the 6 of their papers we have counts for
9 papers
The Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based Revision
Amman Yusuf, Jiwoo Shin, Ruizhi Deng +3
The Safety-Aware Denoiser (SAD) is a recent, training-free, safety-guidance framework for text diffusion models. During inference, SAD steers text samples toward safe regions of th…
FOMO-3D: Using Vision Foundation Models for Long-Tailed 3D Object Detection
Anqi Joyce Yang, James Tu, Nikita Dvornik +2
In order to navigate complex traffic environments, self-driving vehicles must recognize many semantic classes pertaining to vulnerable road users or traffic control devices. Howeve…
Extreme Model Compression with Structured Sparsity at Low Precision
Dan Liu, Nikita Dvornik, Xue Liu
Deep neural networks (DNNs) are used in many applications, but their large size and high computational cost make them hard to run on devices with limited resources. Two widely used…
LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
Anqi Joyce Yang, Sergio Casas, Nikita Dvornik +5
A major bottleneck to scaling-up training of self-driving perception systems are the human annotations required for supervision. A promising alternative is to leverage "auto-labell…
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
Nikita Dvornik, Cordelia Schmid, Julien Mairal
Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new…
Diversity with Cooperation: Ensemble Methods for Few-Shot Classification
Nikita Dvornik, Cordelia Schmid, Julien Mairal
Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples. To solve this challenging prob…
On the Importance of Visual Context for Data Augmentation in Scene Understanding
Nikita Dvornik, Julien Mairal, Cordelia Schmid
Performing data augmentation for learning deep neural networks is known to be important for training visual recognition systems. By artificially increasing the number of training e…
Modeling Visual Context is Key to Augmenting Object Detection Datasets
Nikita Dvornik, Julien Mairal, Cordelia Schmid
Performing data augmentation for learning deep neural networks is well known to be important for training visual recognition systems. By artificially increasing the number of train…
BlitzNet: A Real-Time Deep Network for Scene Understanding
Nikita Dvornik, Konstantin Shmelkov, Julien Mairal +1
Real-time scene understanding has become crucial in many applications such as autonomous driving. In this paper, we propose a deep architecture, called BlitzNet, that jointly perfo…