3 citations · 5 across the 18 of their papers we have counts for
18 papers
Embodied Agents Take Control: Minimal-Interface Zero-Shot Agents Rival Industrial-Scale Policies in Vision-and-Language Navigation
Jian Zhou, Xunyi Zhao, Gengze Zhou +4
Autonomous embodied agents must sustain a long decision-making loop of perceiving, acting, verifying, and self-correcting. In trained end-to-end policies and hand-designed workflow…
Automating the Design of Embodied Agent Architectures
Jian Zhou, Sihao Lin, Jin Li +3
Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules. This modularity exposes a large architectural design space, b…
Ask When It Pays: Cost-Aware Open-Ended Interaction for Instance Goal Navigation
Xunyi Zhao, Sihao Lin, Gengze Zhou +5
Instance Goal Navigation (IGN) requires an embodied agent to find a specific object instance among distractors from an under-specified natural-language description. Such ambiguity…
IntentionNav: A Benchmark for Intent-Driven Object Navigation from Implicit Human Instruction
Lin Qian, Shijie Li, Sihao Lin +4
Existing object navigation benchmarks usually tell an embodied agent which object category to find, such as microwave or chair. Human-facing embodied AI is often asked something le…
One Agent to Guide Them All: Empowering MLLMs for Vision-and-Language Navigation via Explicit World Representation
Zerui Li, Hongpei Zheng, Fangguo Zhao +5
A navigable agent needs to understand both high-level semantic instructions and precise spatial perceptions. Building navigation agents centered on Multimodal Large Language Models…
VLNVerse: A Benchmark for Vision-Language Navigation with Versatile, Embodied, Realistic Simulation and Evaluation
Sihao Lin, Zerui Li, Xunyi Zhao +10
Despite remarkable progress in Vision-Language Navigation (VLN), existing benchmarks remain confined to fixed, small-scale datasets with naive physical simulation. These shortcomin…
Efficient Training for Human Video Generation with Entropy-Guided Prioritized Progressive Learning
Changlin Li, Jiawei Zhang, Shuhao Liu +4
Human video generation has advanced rapidly with the development of diffusion models, but the high computational cost and substantial memory consumption associated with training th…
Freeze, Share, Shrink: Rethinking the Action Backbone in Diffusion Policies
Jian Zhou, Sihao Lin, Shuai Fu +3
Many recent Vision-Language-Action models employ diffusion or flow-matching backbones with hundreds of millions of parameters for action generation. However, unlike image synthesis…
Learning A Zero-shot Occupancy Network from Vision Foundation Models via Self-supervised Adaptation
Sihao Lin, Daqi Liu, Ruochong Fu +6
Estimating the 3D world from 2D monocular images is a fundamental yet challenging task due to the labour-intensive nature of 3D annotations. To simplify label acquisition, this wor…
TransMamba: Fast Universal Architecture Adaption from Transformers to Mamba
Xiuwei Chen, Wentao Hu, Xiao Dong +7
Transformer-based architectures have become the backbone of both uni-modal and multi-modal foundation models, largely due to their scalability via attention mechanisms, resulting i…
Efficient Training of Large Vision Models via Advanced Automated Progressive Learning
Changlin Li, Jiawei Zhang, Sihao Lin +4
The rapid advancements in Large Vision Models (LVMs), such as Vision Transformers (ViTs) and diffusion models, have led to an increasing demand for computational resources, resulti…
Making Large Language Models Better Planners with Reasoning-Decision Alignment
Zhijian Huang, Tao Tang, Shaoxiang Chen +5
Data-driven approaches for autonomous driving (AD) have been widely adopted in the past decade but are confronted with dataset bias and uninterpretability. Inspired by the knowledg…
Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection
Junfei Yi, Jianxu Mao, Tengfei Liu +5
Knowledge distillation (KD) is a widely adopted and effective method for compressing models in object detection tasks. Particularly, feature-based distillation methods have shown r…
MLP Can Be A Good Transformer Learner
Sihao Lin, Pumeng Lyu, Dongrui Liu +4
Self-attention mechanism is the key of the Transformer but often criticized for its computation demands. Previous token pruning works motivate their methods from the view of comput…
Self-Supervised Multi-Frame Neural Scene Flow
Dongrui Liu, Daqi Liu, Xueqian Li +5
Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving. Despite their su…
FULLER: Unified Multi-modality Multi-task 3D Perception via Multi-level Gradient Calibration
Zhijian Huang, Sihao Lin, Guiyu Liu +5
Multi-modality fusion and multi-task learning are becoming trendy in 3D autonomous driving scenario, considering robust prediction and computation budget. However, naively extendin…
Knowledge Distillation via the Target-aware Transformer
Sihao Lin, Hongwei Xie, Bing Wang +4
Knowledge distillation becomes a de facto standard to improve the performance of small neural networks. Most of the previous works propose to regress the representational features…
Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillation
Li Liu, Qingle Huang, Sihao Lin +4
Knowledge Distillation has shown very promising abil-ity in transferring learned representation from the largermodel (teacher) to the smaller one (student).Despitemany efforts, pri…