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Found 16 papers, 14 papers with code
End-to-End Autonomous Driving through V2X Cooperation
Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving.
RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception
The value of roadside perception, which could extend the boundaries of autonomous driving and traffic management, has gradually become more prominent and acknowledged in recent years.
Not all Layers of LLMs are Necessary during Inference
To answer this question, we first indicate that Not all Layers are Necessary during Inference by statistically analyzing the activated layers across tasks.
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection
In autonomous driving, cooperative perception makes use of multi-view cameras from both vehicles and infrastructure, providing a global vantage point with rich semantic context of road conditions beyond a single vehicle viewpoint.
Learning Cooperative Trajectory Representations for Motion Forecasting
Specifically, we present V2X-Graph, the first interpretable and end-to-end learning framework for cooperative motion forecasting.
FLM-101B: An Open LLM and How to Train It with $100K Budget
We demonstrate that a 101B-parameter LLM with 0. 31T tokens can be trained with a budget of 100K US dollars.
QUEST: Query Stream for Practical Cooperative Perception
In this paper, we propose the concept of query cooperation to enable interpretable instance-level flexible feature interaction.
nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales
With around 14% of the one-time pre-training cost, we can accurately forecast the loss for models up to 52B.
VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object Detection
In autonomous driving, Vehicle-Infrastructure Cooperative 3D Object Detection (VIC3D) makes use of multi-view cameras from both vehicles and traffic infrastructure, providing a global vantage point with rich semantic context of road conditions beyond a single vehicle viewpoint.
SpiderMesh: Spatial-aware Demand-guided Recursive Meshing for RGB-T Semantic Segmentation
For semantic segmentation in urban scene understanding, RGB cameras alone often fail to capture a clear holistic topology in challenging lighting conditions.
Ranked #8 on
Thermal Image Segmentation
on PST900
Calibration-free BEV Representation for Infrastructure Perception
Effective BEV object detection on infrastructure can greatly improve traffic scenes understanding and vehicle-toinfrastructure (V2I) cooperative perception.
Ranked #5 on
3D Object Detection
on DAIR-V2X-I
Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation
Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels.
E^2TAD: An Energy-Efficient Tracking-based Action Detector
Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays.
POAR: Efficient Policy Optimization via Online Abstract State Representation Learning
Firstly, We engage RL loss to assist in updating SRL model so that the states can evolve to meet the demand of RL and maintain a good physical interpretation.
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation
For each 3D point, the local polar representation block is firstly explored to construct a spatial representation that is invariant to the z-axis rotation, then the dual-distance attentive pooling block is designed to utilize the representations of its neighbors for learning more discriminative local features according to both the geometric and feature distances among them, and finally, the global contextual feature block is designed to learn a global context for each 3D point by utilizing its spatial location and the volume ratio of the neighborhood to the global point cloud.
Ranked #1 on
Semantic Segmentation
on Toronto-3D L002
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection
Most successful object detectors are anchor-based, which is difficult to adapt to the diversity of traffic objects.
