20 citations · 52 across the 11 of their papers we have counts for
11 papers
Learning from Human Driving: A Human-in-the-Loop Online Behavior Cloning Framework for Autonomous Driving
Yuhong Shi, Jianyi Liu, Lihang Sun +2
With the evolution of large foundation models (LFMs), data-driven autonomous driving has made significant strides. However, existing paradigms still face severe challenges in compl…
SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge
Andong Wang, Bo Wu, Sunli Chen +5
Learning commonsense reasoning from visual contexts and scenes in real-world is a crucial step toward advanced artificial intelligence. However, existing video reasoning benchmarks…
AffordanceLLM: Grounding Affordance from Vision Language Models
Shengyi Qian, Weifeng Chen, Min Bai +3
Affordance grounding refers to the task of finding the area of an object with which one can interact. It is a fundamental but challenging task, as a successful solution requires th…
DAT++: Spatially Dynamic Vision Transformer with Deformable Attention
Zhuofan Xia, Xuran Pan, Shiji Song +2
Transformers have shown superior performance on various vision tasks. Their large receptive field endows Transformer models with higher representation power than their CNN counterp…
GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
Yanjie Ze, Ge Yan, Yueh-Hua Wu +6
It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To ach…
HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Models
Eslam Mohamed Bakr, Pengzhan Sun, Xiaoqian Shen +3
In recent years, Text-to-Image (T2I) models have been extensively studied, especially with the emergence of diffusion models that achieve state-of-the-art results on T2I synthesis…
Improving self-supervised representation learning via sequential adversarial masking
Dylan Sam, Min Bai, Tristan McKinney +1
Recent methods in self-supervised learning have demonstrated that masking-based pretext tasks extend beyond NLP, serving as useful pretraining objectives in computer vision. Howeve…
Policy Adaptation from Foundation Model Feedback
Yuying Ge, Annabella Macaluso, Li Erran Li +2
Recent progress on vision-language foundation models have brought significant advancement to building general-purpose robots. By using the pre-trained models to encode the scene an…
Vision Transformer with Deformable Attention
Zhuofan Xia, Xuran Pan, Shiji Song +2
Transformers have recently shown superior performances on various vision tasks. The large, sometimes even global, receptive field endows Transformer models with higher representati…
3D Object Detection with Pointformer
Xuran Pan, Zhuofan Xia, Shiji Song +2
Feature learning for 3D object detection from point clouds is very challenging due to the irregularity of 3D point cloud data. In this paper, we propose Pointformer, a Transformer…
Video Depth Estimation by Fusing Flow-to-Depth Proposals
Jiaxin Xie, Chenyang Lei, Zhuwen Li +2
Depth from a monocular video can enable billions of devices and robots with a single camera to see the world in 3D. In this paper, we present an approach with a differentiable flow…