1 code implementation • 9 Apr 2024 • Tong Zhao, Lei Yang, Yichen Xie, Mingyu Ding, Masayoshi Tomizuka, Yintao Wei
This paper uniformly proposes two simple yet effective models for road elevation reconstruction in BEV named RoadBEV-mono and RoadBEV-stereo, which estimate road elevation with monocular and stereo images, respectively.
no code implementations • 15 Mar 2024 • Han Lu, Yichen Xie, Xiaokang Yang, Junchi Yan
In this paper, we propose a Bi-Level Active Finetuning framework to select the samples for annotation in one shot, which includes two stages: core sample selection for diversity, and boundary sample selection for uncertainty.
no code implementations • 5 Mar 2024 • Han Lu, Xiaosong Jia, Yichen Xie, Wenlong Liao, Xiaokang Yang, Junchi Yan
End-to-end differentiable learning for autonomous driving (AD) has recently become a prominent paradigm.
no code implementations • 1 Mar 2024 • Han Lu, Siyu Sun, Yichen Xie, Liqing Zhang, Xiaokang Yang, Junchi Yan
In the long-tailed recognition field, the Decoupled Training paradigm has demonstrated remarkable capabilities among various methods.
no code implementations • 23 Feb 2024 • Yichen Xie, Hongge Chen, Gregory P. Meyer, Yong Jae Lee, Eric M. Wolff, Masayoshi Tomizuka, Wei Zhan, Yuning Chai, Xin Huang
Observations from different angles enable the recovery of 3D object states from 2D image inputs if we can identify the same instance in different input frames.
1 code implementation • NeurIPS 2023 • Yichen Xie, Mingyu Ding, Masayoshi Tomizuka, Wei Zhan
However, current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly.
1 code implementation • ICCV 2023 • Yichen Xie, Chenfeng Xu, Marie-Julie Rakotosaona, Patrick Rim, Federico Tombari, Kurt Keutzer, Masayoshi Tomizuka, Wei Zhan
However, given that objects occupy only a small part of a scene, finding dense candidates and generating dense representations is noisy and inefficient.
1 code implementation • CVPR 2023 • Yichen Xie, Han Lu, Junchi Yan, Xiaokang Yang, Masayoshi Tomizuka, Wei Zhan
We propose a novel method called ActiveFT for active finetuning task to select a subset of data distributing similarly with the entire unlabeled pool and maintaining enough diversity by optimizing a parametric model in the continuous space.
no code implementations • 1 Oct 2022 • Zheng Wu, Yichen Xie, Wenzhao Lian, Changhao Wang, Yanjiang Guo, Jianyu Chen, Stefan Schaal, Masayoshi Tomizuka
Experimental results demonstrate that our proposed method achieves policy generalization to unseen compositional tasks in a zero-shot manner.
3 code implementations • 15 Dec 2021 • Yichen Xie, Masayoshi Tomizuka, Wei Zhan
Existing work follows a cumbersome pipeline that repeats the time-consuming model training and batch data selection multiple times.
1 code implementation • ICCV 2021 • Siyuan Huang, Yichen Xie, Song-Chun Zhu, Yixin Zhu
To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by camera views, lighting, occlusions, etc.
Ranked #4 on 3D Object Detection on SUN-RGBD
no code implementations • ICLR 2021 • Hao Zhang, Sen Li, Yinchao Ma, Mingjie Li, Yichen Xie, Quanshi Zhang
Experimental results on various DNNs and datasets have shown that the interaction loss can effectively improve the utility of dropout and boost the performance of DNNs.
no code implementations • 10 Oct 2020 • Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, Quanshi Zhang
In this paper, we define and quantify the significance of interactions among multiple input variables of the DNN.
no code implementations • 2 Oct 2020 • Yichen Xie, Hao-Shu Fang, Dian Shao, Yong-Lu Li, Cewu Lu
Human-object interaction (HOI) detection requires a large amount of annotated data.
Ranked #68 on Domain Generalization on PACS
1 code implementation • 2 Oct 2020 • Hao-Shu Fang, Yichen Xie, Dian Shao, Cewu Lu
On the other hand, existing one-stage methods mainly focus on the union regions of interactions, which introduce unnecessary visual information as disturbances to HOI detection.
Ranked #15 on Human-Object Interaction Detection on V-COCO
no code implementations • 24 Sep 2020 • Hao Zhang, Sen Li, Yinchao Ma, Mingjie Li, Yichen Xie, Quanshi Zhang
This paper aims to understand and improve the utility of the dropout operation from the perspective of game-theoretic interactions.