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Found 10 papers, 6 papers with code
LangProp: A code optimization framework using Language Models applied to driving
LangProp is a framework for iteratively optimizing code generated by large language models (LLMs) in a supervised/reinforcement learning setting.
GAIA-1: A Generative World Model for Autonomous Driving
Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios remains challenging.
Neural World Models for Computer Vision
We present for the first time a model that can predict probabilistic future trajectories of dynamic agents in bird's-eye view from 360{\deg} surround monocular cameras only.
Model-Based Imitation Learning for Urban Driving
Our approach is the first camera-only method that models static scene, dynamic scene, and ego-behaviour in an urban driving environment.
SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting
Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classification.
ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy
Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency.
FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras
We present FIERY: a probabilistic future prediction model in bird's-eye view from monocular cameras.
Ranked #1 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU veh - 224x480 - No vis filter - 100x50 at 0.25 metric)
Probabilistic Future Prediction for Video Scene Understanding
We present a novel deep learning architecture for probabilistic future prediction from video.
Learning a Spatio-Temporal Embedding for Video Instance Segmentation
We present a novel embedding approach for video instance segmentation.
Multimodal Sentiment Analysis To Explore the Structure of Emotions
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing.
