Search Results for author:
Found 4 papers, 1 papers with code
Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection
In this work, our key hypothesis is that this change in loss values during training can be used as a feature to identify anomalous data.
DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos
There are two challenges in this direction: First, rendering error gradients are often insufficient to recover fast object motion, and second, view predictive generative models work much better for objects than whole scenes, so, score distillation objectives cannot currently be applied at the scene level directly.
Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models
This begs the question: can we re-purpose these large-scale pre-trained static image models for open-vocabulary video tracking?
Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification
Few-shot learning (FSL) requires one to learn from object categories with a small amount of training data (as novel classes), while the remaining categories (as base classes) contain a sufficient amount of data for training.
