5 citations · 7 across the 10 of their papers we have counts for
13 papers
Hybrid-Policy Self-Editing for Composable Unstructured Knowledge Editing
Tianci Liu, Zihan Dong, Tianchun Li +8
Large language models (LLMs) achieve remarkable performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a…
PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation
Pengyuan Guo, Zhonghao Mai, Zhengtong Xu +8
Recent advances in vision-language models (VLMs) have enabled increasing progress in real-world robot manipulation. However, long-horizon manipulation in unstructured environments…
In-Context Compositional Learning via Sparse Coding Transformer
Wei Chen, Jingxi Yu, Zichen Miao +1
Transformer architectures have achieved remarkable success across language, vision, and multimodal tasks, and there is growing demand for them to address in-context compositional l…
TALENT: Table VQA via Augmented Language-Enhanced Natural-text Transcription
Guo Yutong, Wanying Wang, Yue Wu +2
Table Visual Question Answering (Table VQA) is typically addressed by large vision-language models (VLMs). While such models can answer directly from images, they often miss fine-g…
Sparse Fine-Tuning of Transformers for Generative Tasks
Wei Chen, Jingxi Yu, Zichen Miao +1
Large pre-trained transformers have revolutionized artificial intelligence across various domains, and fine-tuning remains the dominant approach for adapting these models to downst…
DiffOG: Differentiable Policy Trajectory Optimization with Generalizability
Zhengtong Xu, Zichen Miao, Qiang Qiu +2
Imitation learning-based visuomotor policies excel at manipulation tasks but often produce suboptimal action trajectories compared to model-based methods. Directly mapping camera d…
Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
Zichen Miao, Wei Chen, Qiang Qiu
Transformer-based large pre-trained models have shown remarkable generalization ability, and various parameter-efficient fine-tuning (PEFT) methods have been proposed to customize…
Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization
Zichen Miao, Zhengyuan Yang, Kevin Lin +4
Recent advancements in timestep-distilled diffusion models have enabled high-quality image generation that rivals non-distilled multi-step models, but with significantly fewer infe…
Large Convolutional Model Tuning via Filter Subspace
Wei Chen, Zichen Miao, Qiang Qiu
Efficient fine-tuning methods are critical to address the high computational and parameter complexity while adapting large pre-trained models to downstream tasks. Our study is insp…
Training Bayesian Neural Networks with Sparse Subspace Variational Inference
Junbo Li, Zichen Miao, Qiang Qiu +1
Bayesian neural networks (BNNs) offer uncertainty quantification but come with the downside of substantially increased training and inference costs. Sparse BNNs have been investiga…
Adaptive Convolutions with Per-pixel Dynamic Filter Atom
Ze Wang, Zichen Miao, Jun Hu +1
Applying feature dependent network weights have been proved to be effective in many fields. However, in practice, restricted by the enormous size of model parameters and memory foo…
Graph Convolution with Low-rank Learnable Local Filters
Xiuyuan Cheng, Zichen Miao, Qiang Qiu
Geometric variations like rotation, scaling, and viewpoint changes pose a significant challenge to visual understanding. One common solution is to directly model certain intrinsic…
AED-Net: An Abnormal Event Detection Network
Tian Wang, Zichen Miao, Yuxin Chen +3
It is challenging to detect the anomaly in crowded scenes for quite a long time. In this paper, a self-supervised framework, abnormal event detection network (AED-Net), which is co…