2 citations · 4 across the 9 of their papers we have counts for
11 papers
DK-VLM: Geometry as Position for Spatial Reasoning in Vision-Language Models
Han Su, Meng Tian, Tianyu Huang +7
Vision-language models still struggle to understand 3D spatial relations across views and update spatial judgments as the observer moves. A common approach fuses 3D reconstruction…
4D-WAM: 4D Consistent World Modeling for Autonomous Driving
Jiacheng Fu, Yibo Yuan, Meng Tian +8
Emerging World-Action Models (WAMs) have demonstrated promising performance in autonomous driving by jointly modeling future driving scene evolution and trajectory planning. Howeve…
SUV: Future Scene Understanding as Video Generation for End-to-End Driving
Yibo Yuan, Jiacheng Fu, Jiangtong Zhu +8
End-to-end driving requires a coherent understanding of future scenes, yet existing methods model these scenes using task-specific heads and output formats, with limited scalabilit…
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
SkinFlow: Efficient Information Transmission for Open Dermatological Diagnosis via Dynamic Visual Encoding and Staged RL
Lijun Liu, Linwei Chen, Zhishou Zhang +7
General-purpose Large Vision-Language Models (LVLMs), despite their massive scale, often falter in dermatology due to "diffuse attention" - the inability to disentangle subtle path…
Percept-WAM: Perception-Enhanced World-Awareness-Action Model for Robust End-to-End Autonomous Driving
Jianhua Han, Meng Tian, Jiangtong Zhu +16
Autonomous driving heavily relies on accurate and robust spatial perception. Many failures arise from inaccuracies and instability, especially in long-tail scenarios and complex in…
StreamForest: Efficient Online Video Understanding with Persistent Event Memory
Xiangyu Zeng, Kefan Qiu, Qingyu Zhang +9
Multimodal Large Language Models (MLLMs) have recently achieved remarkable progress in video understanding. However, their effectiveness in real-time streaming scenarios remains li…
Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
Huizhen Shu, Xuying Li, Qirui Wang +3
With the rapid proliferation of Natural Language Processing (NLP), especially Large Language Models (LLMs), generating adversarial examples to jailbreak LLMs remains a key challeng…
Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning
Yue Li, Meng Tian, Dechang Zhu +4
Large vision-language models (VLMs) for autonomous driving (AD) are evolving beyond perception and cognition tasks toward motion planning. However, we identify two critical challen…
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving
Yue Li, Meng Tian, Zhenyu Lin +7
Existing benchmarks for Vision-Language Model (VLM) on autonomous driving (AD) primarily assess interpretability through open-form visual question answering (QA) within coarse-grai…
Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases
Kai Chen, Yanze Li, Wenhua Zhang +10
Large Vision-Language Models (LVLMs) have received widespread attention for advancing the interpretable self-driving. Existing evaluations of LVLMs primarily focus on multi-faceted…