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Found 16 papers, 9 papers with code
An Extensible Framework for Open Heterogeneous Collaborative Perception
In this paper, we introduce a new open heterogeneous problem: how to accommodate continually emerging new heterogeneous agent types into collaborative perception, while ensuring high perception performance and low integration cost?
Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals
Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving.
The Efficiency Spectrum of Large Language Models: An Algorithmic Survey
The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains, reshaping the artificial general intelligence landscape.
MMVP: Motion-Matrix-based Video Prediction
A central challenge of video prediction lies where the system has to reason the objects' future motions from image frames while simultaneously maintaining the consistency of their appearances across frames.
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study
Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series.
Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction
We instead propose fine-grained task that treats each RGB-D pair as a task in our meta-optimization.
Ranked #33 on
Monocular Depth Estimation
on NYU-Depth V2
TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving
Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial-temporal priors.
Ranked #2 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU ped - 224x480 - Vis filter. - 100x100 at 0.5 metric)
Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps
Where2comm has two distinct advantages: i) it considers pragmatic compression and uses less communication to achieve higher perception performance by focusing on perceptually critical areas; and ii) it can handle varying communication bandwidth by dynamically adjusting spatial areas involved in communication.
Ranked #1 on
Monocular 3D Object Detection
on CoPerception-UAVs
Aware of the History: Trajectory Forecasting with the Local Behavior Data
In this work, we re-introduce this information as a new type of input data for trajectory forecasting systems: the local behavior data, which we conceptualize as a collection of location-specific historical trajectories.
Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting
Further, we apply the proposed framework to current SOTA multi-agent multi-modal forecasting systems as a plugin module, which enables the SOTA systems to 1) estimate the uncertainty in the multi-agent multi-modal trajectory forecasting task; 2) rank the multiple predictions and select the optimal one based on the estimated uncertainty.
Exploiting the Potential of Datasets: A Data-Centric Approach for Model Robustness
Robustness of deep neural networks (DNNs) to malicious perturbations is a hot topic in trustworthy AI.
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector.
V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving
Vehicle-to-everything (V2X) communication techniques enable the collaboration between vehicles and many other entities in the neighboring environment, which could fundamentally improve the perception system for autonomous driving.
Behind the Curtain: Learning Occluded Shapes for 3D Object Detection
Finally, the probability of occupancy is also integrated into a proposal refinement module to generate the final bounding boxes.
Ranked #2 on
3D Object Detection
on KITTI Cars Moderate
Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
2) The results of trajectory forecasting benchmarks demonstrate that the CU-based framework steadily helps SOTA systems improve their performances.
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion
Such a transformation enables CFCNet to predict features and reconstruct data of missing depth measurements according to their corresponding, transformed RGB features.
