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Found 7 papers, 0 papers with code
V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions
Current LiDAR-based Vehicle-to-Everything (V2X) multi-agent perception systems have shown the significant success on 3D object detection.
Bifurcated Attention for Single-Context Large-Batch Sampling
In our study, we present bifurcated attention, a method developed for language model inference in single-context batch sampling contexts.
EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV
One white-box and two black-box patch based attack methods are implemented to attack three classic deep neural networks based object detectors on EVD4UAV.
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be exponentially hard in the worst-case.
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP
In this paper, we study representation learning in partially observable Markov Decision Processes (POMDPs), where the agent learns a decoder function that maps a series of high-dimensional raw observations to a compact representation and uses it for more efficient exploration and planning.
Tracking without Label: Unsupervised Multiple Object Tracking via Contrastive Similarity Learning
Unsupervised learning is a challenging task due to the lack of labels.
Adaptive Remote Sensing Image Attribute Learning for Active Object Detection
In recent years, deep learning methods bring incredible progress to the field of object detection.
