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Found 19 papers, 13 papers with code
Fast-DataShapley: Neural Modeling for Training Data Valuation
Shapley value, a potent tool for evaluating contributions, outperforms other methods in theory, but its computational overhead escalates exponentially with the number of data providers.
GDI-Bench: A Benchmark for General Document Intelligence with Vision and Reasoning Decoupling
We evaluate the GDI-Bench on various open-source and closed-source models, conducting decoupled analyses in the visual and reasoning domains.
GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training
Given the significant differences between geometric diagram-symbol and natural image-text, we introduce unimodal pre-training to develop a diagram encoder and symbol decoder, enhancing the understanding of geometric images and corpora.
Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving
Experiments also demonstrate that as the memory bank expands, the Heuristic Process with only 1. 8B parameters can inherit the knowledge from a GPT-4 powered Analytic Process and achieve continuous performance improvement.
A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks.
OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving
With the development of implicit rendering technology and in-depth research on using generative models to produce data at scale, we propose OASim, an open and adaptive simulator and autonomous driving data generator based on implicit neural rendering.
Realistic Rainy Weather Simulation for LiDARs in CARLA Simulator
To this end, we propose a simulator-based physical modeling approach to augment LiDAR data in rainy weather in order to improve the perception performance of LiDAR in this scenario.
Towards Knowledge-driven Autonomous Driving
This paper explores the emerging knowledge-driven autonomous driving technologies.
SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Models
In this paper, we propose a novel framework based on diffusion models, called SceneDM, to generate joint and consistent future motions of all the agents, including vehicles, bicycles, pedestrians, etc., in a scene.
On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving
This has been a significant bottleneck, particularly in the development of common sense reasoning and nuanced scene understanding necessary for safe and reliable autonomous driving.
Specify Robust Causal Representation from Mixed Observations
Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the whole system, we can improve the robustness and generalization performance of machine learning models.
Optimizing the Placement of Roadside LiDARs for Autonomous Driving
We define perceptual gain as the increased perceptual capability when a new LiDAR is placed.
DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models
Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretability.
Ranked #1 on
10-shot image generation
on FQL-Driving
SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations
Annotating 3D LiDAR point clouds for perception tasks is fundamental for many applications e. g., autonomous driving, yet it still remains notoriously labor-intensive.
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation
Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous domain knowledge can be hardly directly deployed to a new domain without additional costs.
Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library
Through simulating point cloud data in different LiDAR placements, we can evaluate the perception accuracy of these placements using multiple detection models.
OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving
To this end, we present OpenCalib, a calibration toolbox that contains a rich set of various sensor calibration methods.
CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene
Sensor-based environmental perception is a crucial part of the autonomous driving system.
Generalizable Information Theoretic Causal Representation
It is evidence that representation learning can improve model's performance over multiple downstream tasks in many real-world scenarios, such as image classification and recommender systems.
