Search Results for author:
Found 9 papers, 4 papers with code
AVR: Synergizing Foundation Models for Audio-Visual Humor Detection
In this work, we present, AVR application for audio-visual humor detection.
Estimation of Appearance and Occupancy Information in Birds Eye View from Surround Monocular Images
To overcome this limitation, we propose a novel representation that captures various traffic participants appearance and occupancy information from an array of monocular cameras covering 360 deg field of view (FOV).
NMR: Neural Manifold Representation for Autonomous Driving
To overcome this limitation, we propose Neural Manifold Representation (NMR), a representation for the task of autonomous driving that learns to infer semantics and predict way-points on a manifold over a finite horizon, centered on the ego-vehicle.
Deep Implicit Surface Point Prediction Networks
Unsigned distance function (UDF) based approaches have been proposed recently as a promising alternative to represent both open and closed shapes.
DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfaces
Several implicit 3D shape representation approaches using deep neural networks have been proposed leading to significant improvements in both quality of representations as well as the impact on downstream applications.
INFER: INtermediate representations for FuturE pRediction
Uncharacteristic of state-of-the-art approaches, our representations and models generalize to completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving).
The Earth ain't Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving Camera
The proposed approach significantly improves the state-of-the-art for monocular object localization on arbitrarily-shaped roads.
Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking
This paper introduces geometry and object shape and pose costs for multi-object tracking in urban driving scenarios.
Ranked #21 on
Multiple Object Tracking
on KITTI Test (Online Methods)
(MOTA metric)
ECHO: An Adaptive Orchestration Platform for Hybrid Dataflows across Cloud and Edge
ECHO can schedule the dataflow on different Edge, Fog and Cloud resources, and also perform dynamic task migration between resources.
Distributed, Parallel, and Cluster Computing
