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DART: Differentiable Dynamic Adaptive Region Tokenizer for Vision Foundation Models
Shicheng Yin, Kaixuan Yin, Yang Liu +2
The content-agnostic, fixed-grid tokenizers used by standard large-scale vision models like Vision Transformer (ViT) and Vision Mamba (Vim) represent a fundamental performance bott…
3DAffordSplat: Efficient Affordance Reasoning with 3D Gaussians
Zeming Wei, Junyi Lin, Yang Liu +4
3D affordance reasoning is essential in associating human instructions with the functional regions of 3D objects, facilitating precise, task-oriented manipulations in embodied AI.…
DSPNet: Dual-vision Scene Perception for Robust 3D Question Answering
Jingzhou Luo, Yang Liu, Weixing Chen +4
3D Question Answering (3D QA) requires the model to comprehensively understand its situated 3D scene described by the text, then reason about its surrounding environment and answer…
Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering
Kaixuan Jiang, Yang Liu, Weixing Chen +5
Embodied Question Answering (EQA) is a challenging task in embodied intelligence that requires agents to dynamically explore 3D environments, actively gather visual information, an…
VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis
Shicheng Yin, Kaixuan Yin, Weixing Chen +2
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) are two dominant models for image analysis. While CNNs excel at extracting multi-scale features and ViTs effecti…
Towards Long-Horizon Vision-Language Navigation: Platform, Benchmark and Method
Xinshuai Song, Weixing Chen, Yang Liu +3
Existing Vision-Language Navigation (VLN) methods primarily focus on single-stage navigation, limiting their effectiveness in multi-stage and long-horizon tasks within complex and…