Search Results for author:
Found 7 papers, 1 papers with code
LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning
Although planning is a crucial component of the autonomous driving stack, researchers have yet to develop robust planning algorithms that are capable of safely handling the diverse range of possible driving scenarios.
OmniLabel: A Challenging Benchmark for Language-Based Object Detection
With more than 28K unique object descriptions on over 25K images, OmniLabel provides a challenging benchmark with diverse and complex object descriptions in a naturally open-vocabulary setting.
STRIVE: Scene Text Replacement In Videos
Finally, the new text is transferred from the reference to remaining frames using a novel learned image transformation network that captures lighting and blur effects in a temporally consistent manner.
Bayesian Semantic Instance Segmentation in Open Set World
In this paper, we present a novel open-set semantic instance segmentation approach capable of segmenting all known and unknown object classes in images, based on the output of an object detector trained on known object classes.
Smart Mining for Deep Metric Learning
In this paper, we propose a novel deep metric learning method that combines the triplet model and the global structure of the embedding space.
DeepSetNet: Predicting Sets with Deep Neural Networks
This paper addresses the task of set prediction using deep learning.
Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions
Current results from machine learning show that replacing this siamese by a triplet network can improve the classification accuracy in several problems, but this has yet to be demonstrated for local image descriptor learning.
