# Language-Driven Active Learning for Diverse Open-Set 3D Object Detection

arXiv: https://arxiv.org/abs/2404.12856 · HTML: https://mlsanity.com/paper/2404.12856/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 49 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 30 of 32)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework](https://mlsanity.com/paper/2205.13790/) (2022)
- [Ground-aware Monocular 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2102.00690/) (2021)
- [Occupancy Flow Fields for Motion Forecasting in Autonomous Driving](https://mlsanity.com/paper/2203.03875/) (2022)
- [Scalable Active Learning for Object Detection](https://mlsanity.com/paper/2004.04699/) (2020)
- [ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2107.11769/) (2021)
- [PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians](https://mlsanity.com/paper/1805.11833/) (2018)
- [Label Efficient Visual Abstractions for Autonomous Driving](https://mlsanity.com/paper/2005.10091/) (2020)
- [Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss](https://mlsanity.com/paper/2011.06679/) (2020)
- [3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality Attention](https://mlsanity.com/paper/2203.10926/) (2022)
- [Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets](https://mlsanity.com/paper/2402.07320/) (2024)
- [The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration](https://mlsanity.com/paper/2401.16634/) (2024)
- [ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2402.03235/) (2024)
- [Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context](https://mlsanity.com/paper/2301.05804/) (2023)
- [On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset](https://mlsanity.com/paper/2112.00942/) (2021)
- [Interactive Joint Planning for Autonomous Vehicles](https://mlsanity.com/paper/2310.18301/) (2023)
- [Robust Traffic Light Detection Using Salience-Sensitive Loss: Computational Framework and Evaluations](https://mlsanity.com/paper/2305.04516/) (2023)
- [Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning](https://mlsanity.com/paper/2307.08415/) (2023)
- [Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles](https://mlsanity.com/paper/2302.08292/) (2023)
- [Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences](https://mlsanity.com/paper/2302.09043/) (2023)
- [Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis](https://mlsanity.com/paper/2305.04506/) (2023)
- [Multi-Task Consistency for Active Learning](https://mlsanity.com/paper/2306.12398/) (2023)
- [Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales](https://mlsanity.com/paper/2304.13147/) (2023)

## Built on by (most cited first, 15 of 15)

- [Can Vision-Language Models Understand and Interpret Dynamic Gestures from Pedestrians? Pilot Datasets and Exploration Towards Instructive Nonverbal Commands for Cooperative Autonomous Vehicles](https://mlsanity.com/paper/2504.10873/) (2025)
- [Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving Safety](https://mlsanity.com/paper/2504.13399/) (2025)
- [Technical Report for Argoverse2 Scenario Mining Challenges on Iterative Error Correction and Spatially-Aware Prompting](https://mlsanity.com/paper/2506.11124/) (2025)
- [Natural Language Instructions for Scene-Responsive Human-in-the-Loop Motion Planning in Autonomous Driving using Vision-Language-Action Models](https://mlsanity.com/paper/2602.04184/) (2026)
- [Driver Activity Classification Using Generalizable Representations from Vision-Language Models](https://mlsanity.com/paper/2404.14906/) (2024)
- [Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model](https://mlsanity.com/paper/2408.04917/) (2024)
- [Evaluating Cascaded Methods of Vision-Language Models for Zero-Shot Detection and Association of Hardhats for Increased Construction Safety](https://mlsanity.com/paper/2410.12225/) (2024)
- [A New Perspective On AI Safety Through Control Theory Methodologies](https://mlsanity.com/paper/2506.23703/) (2025)
- [Vision and Language: Novel Representations and Artificial intelligence for Driving Scene Safety Assessment and Autonomous Vehicle Planning](https://mlsanity.com/paper/2602.07680/) (2026)
- [When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems](https://mlsanity.com/paper/2606.29115/) (2026)
- [Finding the Reflection Point: Unpadding Images to Remove Data Augmentation Artifacts in Large Open Source Image Datasets for Machine Learning](https://mlsanity.com/paper/2504.03168/) (2025)
- [Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models](https://mlsanity.com/paper/2505.09139/) (2025)
- [FitPro: A Zero-Shot Framework for Interactive Text-based Pedestrian Retrieval in Open World](https://mlsanity.com/paper/2509.16674/) (2025)
- [Looking and Listening Inside and Outside: Multimodal Artificial Intelligence Systems for Driver Safety Assessment and Intelligent Vehicle Decision-Making](https://mlsanity.com/paper/2602.07668/) (2026)
- [Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language](https://mlsanity.com/paper/2609.03677/) (2026)

## Abstract (arXiv)

Object detection is crucial for ensuring safe autonomous driving. However, data-driven approaches face challenges when encountering minority or novel objects in the 3D driving scene. In this paper, we propose VisLED, a language-driven active learning framework for diverse open-set 3D Object Detection. Our method leverages active learning techniques to query diverse and informative data samples from an unlabeled pool, enhancing the model's ability to detect underrepresented or novel objects. Specifically, we introduce the Vision-Language Embedding Diversity Querying (VisLED-Querying) algorithm, which operates in both open-world exploring and closed-world mining settings. In open-world exploring, VisLED-Querying selects data points most novel relative to existing data, while in closed-world mining, it mines novel instances of known classes. We evaluate our approach on the nuScenes dataset and demonstrate its efficiency compared to random sampling and entropy-querying methods. Our results show that VisLED-Querying consistently outperforms random sampling and offers competitive performance compared to entropy-querying despite the latter's model-optimality, highlighting the potential of VisLED for improving object detection in autonomous driving scenarios. We make our code publicly available at https://github.com/Bjork-crypto/VisLED-Querying