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From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking
Xiaohao Xu, Tianyi Zhang, Sibo Wang +6
Embodied agents require robust navigation systems to operate in unstructured environments, making the robustness of Simultaneous Localization and Mapping (SLAM) models critical to…
-Bench: Benchmarking the Robustness of Referring Perception Models under Perturbations
Xiang Li, Kai Qiu, Jinglu Wang +6
Referring perception, which aims at grounding visual objects with multimodal referring guidance, is essential for bridging the gap between humans, who provide instructions, and the…
AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition
Zhaorun Chen, Zhuokai Zhao, Zhihong Zhu +4
Recent advancements in large language models (LLMs) have shown promise in multi-step reasoning tasks, yet their reliance on extensive manual labeling to provide procedural feedback…
Evaluating and Improving Continual Learning in Spoken Language Understanding
Muqiao Yang, Xiang Li, Umberto Cappellazzo +2
Continual learning has emerged as an increasingly important challenge across various tasks, including Spoken Language Understanding (SLU). In SLU, its objective is to effectively h…
Customizable Perturbation Synthesis for Robust SLAM Benchmarking
Xiaohao Xu, Tianyi Zhang, Sibo Wang +6
Robustness is a crucial factor for the successful deployment of robots in unstructured environments, particularly in the domain of Simultaneous Localization and Mapping (SLAM). Sim…
A General Framework for Learning from Weak Supervision
Hao Chen, Jindong Wang, Lei Feng +6
Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algor…