202 citations · 514 across the 22 of their papers we have counts for
22 papers
BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout
Kairui Yang, Enhui Ma, Jibin Peng +3
Using synthesized images to boost the performance of perception models is a long-standing research challenge in computer vision. It becomes more eminent in visual-centric autonomou…
FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving
Tengju Ye, Wei Jing, Chunyong Hu +11
Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such…
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
Xiaoyang Wu, Zhuotao Tian, Xin Wen +4
The rapid advancement of deep learning models often attributes to their ability to leverage massive training data. In contrast, such privilege has not yet fully benefited 3D deep l…
Dyn-E: Local Appearance Editing of Dynamic Neural Radiance Fields
Shangzan Zhang, Sida Peng, Yinji ShenTu +5
Recently, the editing of neural radiance fields (NeRFs) has gained considerable attention, but most prior works focus on static scenes while research on the appearance editing of d…
BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection
Lei Yang, Kaicheng Yu, Tao Tang +5
While most recent autonomous driving system focuses on developing perception methods on ego-vehicle sensors, people tend to overlook an alternative approach to leverage intelligent…
LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields
Tang Tao, Longfei Gao, Guangrun Wang +7
We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel v…
Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask
Shangzan Zhang, Sida Peng, Tianrun Chen +5
We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single ima…
DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling
Bicheng Guo, Shuxuan Guo, Miaojing Shi +4
Differentiable architecture search (DARTS) has been a mainstream direction in automatic machine learning. Since the discovery that original DARTS will inevitably converge to poor a…
Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers
Tao Tang, Changlin Li, Guangrun Wang +3
Automatic data augmentation (AutoAugment) strategies are indispensable in supervised data-efficient training protocols of vision transformers, and have led to state-of-the-art resu…
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection
Kaicheng Yu, Tang Tao, Hongwei Xie +10
There are two critical sensors for 3D perception in autonomous driving, the camera and the LiDAR. The camera provides rich semantic information such as color, texture, and the LiDA…
BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework
Tingting Liang, Hongwei Xie, Kaicheng Yu +6
Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to lever…
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…
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy
Yash Mehta, Colin White, Arber Zela +6
The release of tabular benchmarks, such as NAS-Bench-101 and NAS-Bench-201, has significantly lowered the computational overhead for conducting scientific research in neural archit…
An Analysis of Super-Net Heuristics in Weight-Sharing NAS
Kaicheng Yu, René Ranftl, Mathieu Salzmann
Weight sharing promises to make neural architecture search (NAS) tractable even on commodity hardware. Existing methods in this space rely on a diverse set of heuristics to design…
Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search
Kaicheng Yu, Rene Ranftl, Mathieu Salzmann
Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically s…
How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS
Kaicheng Yu, Rene Ranftl, Mathieu Salzmann
Weight sharing promises to make neural architecture search (NAS) tractable even on commodity hardware. Existing methods in this space rely on a diverse set of heuristics to design…
Recurrent U-Net for Resource-Constrained Segmentation
Wei Wang, Kaicheng Yu, Joachim Hugonot +2
State-of-the-art segmentation methods rely on very deep networks that are not always easy to train without very large training datasets and tend to be relatively slow to run on sta…
Overcoming Multi-Model Forgetting
Yassine Benyahia, Kaicheng Yu, Kamil Bennani-Smires +4
We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performan…
Evaluating the Search Phase of Neural Architecture Search
Kaicheng Yu, Christian Sciuto, Martin Jaggi +2
Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and val…
Beyond One Glance: Gated Recurrent Architecture for Hand Segmentation
Wei Wang, Kaicheng Yu, Joachim Hugonot +2
As mixed reality is gaining increased momentum, the development of effective and efficient solutions to egocentric hand segmentation is becoming critical. Traditional segmentation…
Statistically Motivated Second Order Pooling
Kaicheng Yu, Mathieu Salzmann
Second-order pooling, a.k.a.~bilinear pooling, has proven effective for deep learning based visual recognition. However, the resulting second-order networks yield a final represent…
Second-order Convolutional Neural Networks
Kaicheng Yu, Mathieu Salzmann
Convolutional Neural Networks (CNNs) have been successfully applied to many computer vision tasks, such as image classification. By performing linear combinations and element-wise…