1 citations · 1 across the 9 of their papers we have counts for
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RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training
Yunshuang Nie, Bingqian Lin, Minzhe Niu +7
Pre-trained Multi-modal Large Language Models (MLLMs) provide a knowledge-rich foundation for post-training by leveraging their inherent perception and reasoning capabilities to so…
UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding
Yueming Xu, Jiahui Zhang, Ze Huang +12
Despite the impressive progress on understanding and generating images shown by the recent unified architectures, the integration of 3D tasks remains challenging and largely unexpl…
4D-VLA: Spatiotemporal Vision-Language-Action Pretraining with Cross-Scene Calibration
Jiahui Zhang, Yurui Chen, Yueming Xu +8
Leveraging diverse robotic data for pretraining remains a critical challenge. Existing methods typically model the dataset's action distribution using simple observations as inputs…
From Flatland to Space: Teaching Vision-Language Models to Perceive and Reason in 3D
Jiahui Zhang, Yurui Chen, Yanpeng Zhou +10
Recent advances in LVLMs have improved vision-language understanding, but they still struggle with spatial perception, limiting their ability to reason about complex 3D scenes. Unl…