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Congcong Li

http://chenlab.ece.cornell.edu/people/congcong/

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    Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving
    Jingxiao Zheng
    Xinwei Shi
    Alexander Gorban
    Junhua Mao
    Yang Song
    Charles Qi
    Ting Liu
    Visesh Chari
    Andre Cornman
    Yin Zhou
    Congcong Li
    Dragomir Anguelov
    CVPR'2022, Workshop on Autonomous Driving, IEEE
    Preview abstract 3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps, failure modes for LiDAR, relative location between the camera and LiDAR, and a high bar for estimation accuracy. Data collected for other use cases (such as virtual reality, gaming, and animation) may therefore not be usable for AV applications. This necessitates the collection and annotation of a large amount of 3D data for HPE in AV, which is time-consuming and expensive. In this paper, we propose one of the first approaches to alleviate this problem in the AV setting. Specifically, we propose a multi-modal approach which uses 2D labels on RGB images as weak supervision to perform 3D HPE. The proposed multi-modal architecture incorporates LiDAR and camera inputs with an auxiliary segmentation branch. On the Waymo Open Dataset, our approach achieves a 22% relative improvement over camera-only 2D HPE baseline, and 6% improvement over LiDAR-only model. Finally, careful ablation studies and parts based analysis illustrate the advantages of each of our contributions. View details
    Improving 3D Object Detection through Progressive Population Based Augmentation
    Shuyang Cheng
    Zhaoqi Leng
    Ekin Dogus Cubuk
    Barret Richard Zoph
    Chunyan Bai
    Jiquan Ngiam
    Yang Song
    Ben Caine
    Vijay Vasudevan
    Congcong Li
    Quoc V. Le
    Jon Shlens
    Drago Anguelov
    ECCV'2020
    Preview abstract Data augmentation has been widely adopted for object detection in 3-D point clouds. All efforts have focused on manually designing specific data augmentation methods for individual architectures, however no work has attempted to automate the design of data augmentation in 3-D detection problems -- as is common in 2-D camera-based computer vision. In this work, we present a first attempt to automate the design of data augmentation policies for 3-D object detection. We describe an algorithm termed Progressive Population Based Augmentation (PPBA). PPBA learns to optimize augmentation strategies by narrowing down the search space, and adopting the best parameters discovered in previous iterations. On the KITTI test set, PPBA improves the StarNet by substantial margins on the moderate difficulty category of cars, pedestrians, and cyclists, outperforming all current state-of-the-art single-stage detection models. Additional experiments on the Waymo Open Dataset, a 20x larger dataset compared to KITTI, indicate that PPBA continues to effectively improve 3D object detection. The magnitude of the improvements may be comparable to advances in 3-D perception architectures, yet data augmentation incurs no cost at inference time. In subsequent experiments, we find that PPBA may be up to 10x more data efficient on baseline 3D detection models without augmentation, highlighting that 3D detection models may achieve competitive accuracy with far fewer labeled examples. View details
    Learning semantic relationships for better action retrieval in images
    Vignesh Ramanathan
    Congcong Li
    Jia Deng
    Wei Han
    Zhen Li
    Kunlong Gu
    Yang Song
    Samy Bengio
    Chuck Rosenberg
    Li Fei-Fei
    CVPR (2015)
    Preview abstract Human actions capture a wide variety of interactions between people and objects. As a result, the set of possible actions is extremely large and it is difficult to obtain sufficient training examples for all actions. However, we could compensate for this sparsity in supervision by leveraging the rich semantic relationship between different actions. A single action is often composed of other smaller actions and is exclusive of certain others. We need a method which can reason about such relationships and extrapolate unobserved actions from known actions. Hence, we propose a novel neural network framework which jointly extracts the relationship between actions and uses them for training better action retrieval models. Our model incorporates linguistic, visual and logical consistency based cues to effectively identify these relationships. We train and test our model on a largescale image dataset of human actions. We show a significant improvement in mean AP compared to different baseline methods including the HEX-graph approach from Deng et al. [8] View details
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