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20232026
most citedDiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

39 citations · 57 across the 12 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

ArmorOCR: Grounded Adversarial Visual Perception via Observation-Transferred Self-Distillation

Linhan Cao, Siyuan Li, Jun Lan +8

Large multimodal models (LMMs) have demonstrated strong OCR recognition capabilities, yet remain vulnerable to adversarial visual text that is readable to humans but challenging fo…

cs.CV2026

Bridging the 2D-3D Gap: A Hierarchical Semantic-Geometric Map for Vision Language Navigation

Kailing Li, Tianwen Qian, Lijin Yang +4

Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions. Despite recent progress with vision-la…

cs.CV2026

Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

Lai Wei, Liangbo He, Jun Lan +9

Multimodal Large Language Models (MLLMs) excel at broad visual understanding but still struggle with fine-grained perception, where decisive evidence is small and easily overwhelme…

cs.CV2025

ToG-Bench: Task-Oriented Spatio-Temporal Grounding in Egocentric Videos

Qi'ao Xu, Tianwen Qian, Yuqian Fu +5

A core capability towards general embodied intelligence lies in localizing task-relevant objects from an egocentric perspective, formulated as Spatio-Temporal Video Grounding (STVG…

cs.CV2023★ 1 cited

3D Multiple Object Tracking on Autonomous Driving: A Literature Review

Peng Zhang, Xin Li, Liang He +1

3D multi-object tracking (3D MOT) stands as a pivotal domain within autonomous driving, experiencing a surge in scholarly interest and commercial promise over recent years. Despite…

cs.CV2023★ 2 cited

DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds

Tao Ma, Xuemeng Yang, Hongbin Zhou +9

Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D…