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Found 18 papers, 7 papers with code
Why Ask One When You Can Ask $k$? Two-Stage Learning-to-Defer to a Set of Experts
We propose Top-$k$ Learning-to-Defer, a generalization of the classical two-stage L2D framework that allocates each query to the $k$ most confident agents instead of a single one.
EventFly: Event Camera Perception from Ground to the Sky
Cross-platform adaptation in event-based dense perception is crucial for deploying event cameras across diverse settings, such as vehicles, drones, and quadrupeds, each with unique motion dynamics, viewpoints, and class distributions.
Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees
Learning-to-Defer (L2D) facilitates optimal task allocation between AI systems and decision-makers.
Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made Scenes
3D Gaussian Splatting (3DGS) has emerged as a promising representation for photorealistic rendering of 3D scenes.
GSVC: Efficient Video Representation and Compression Through 2D Gaussian Splatting
3D Gaussian splats have emerged as a revolutionary, effective, learned representation for static 3D scenes.
FlexEvent: Event Camera Object Detection at Arbitrary Frequencies
Event cameras offer unparalleled advantages for real-time perception in dynamic environments, thanks to their microsecond-level temporal resolution and asynchronous operation.
A Two-Stage Learning-to-Defer Approach for Multi-Task Learning
The Two-Stage Learning-to-Defer framework has been extensively studied for classification and, more recently, regression tasks.
Learning-to-Defer for Extractive Question Answering
Pre-trained language models have profoundly impacted the field of extractive question-answering, leveraging large-scale textual corpora to enhance contextual language understanding.
LapisGS: Layered Progressive 3D Gaussian Splatting for Adaptive Streaming
The rise of Extended Reality (XR) requires efficient streaming of 3D online worlds, challenging current 3DGS representations to adapt to bandwidth-constrained environments.
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles.
Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving
Efficient data utilization is crucial for advancing 3D scene understanding in autonomous driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully supervised methods.
OpenESS: Event-based Semantic Scene Understanding with Open Vocabularies
Event-based semantic segmentation (ESS) is a fundamental yet challenging task for event camera sensing.
CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening
CAESAR is an aggregation strategy used by the server that combines convergence-aware sampling with a screening mechanism.
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
Depth estimation from monocular images is pivotal for real-world visual perception systems.
Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages
We then used this same pipeline to generate messages using GPT-4, and compared the collective diversity of messages from: (1) crowd-writers, (2) GPT-4 using the pipeline, and (3 & 4) two baseline GPT-4 prompts.
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
More recently, with the rapid development of language models (LMs), researchers have focused on leveraging LMs to facilitate the learning of TGs, either by jointly training them in a computationally intensive framework (merging the two stages), or designing complex self-supervised training tasks for feature extraction (enhancing the first stage).
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The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
Shape-CD: Change-Point Detection in Time-Series Data with Shapes and Neurons
We found that existing approaches become less accurate when the underlying process is complex and generates large varieties of patterns in the time series.
