Author name cluster

Jiefeng Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

AAAI Conference 2024 Conference Paper

ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation

Siyuan Bian
Jiefeng Li
Jiasheng Tang
Cewu Lu

Accurate human shape recovery from a monocular RGB image is a challenging task because humans come in different shapes and sizes and wear different clothes. In this paper, we propose ShapeBoost, a new human shape recovery framework that achieves pixel-level alignment even for rare body shapes and high accuracy for people wearing different types of clothes. Unlike previous approaches that rely on the use of PCA-based shape coefficients, we adopt a new human shape parameterization that decomposes the human shape into bone lengths and the mean width of each part slice. This part-based parameterization technique achieves a balance between flexibility and validity using a semi-analytical shape reconstruction algorithm. Based on this new parameterization, a clothing-preserving data augmentation module is proposed to generate realistic images with diverse body shapes and accurate annotations. Experimental results show that our method outperforms other state-of-the-art methods in diverse body shape situations as well as in varied clothing situations.

PDF Details DOI

AAAI Conference 2022 Conference Paper

Correlation Field for Boosting 3D Object Detection in Structured Scenes

Jianhua Sun
Hao-Shu Fang
Xianghui Zhu
Jiefeng Li
Cewu Lu

Data augmentation is an efficient way to elevate 3D object detection performance. In this paper, we propose a simple but effective online crop-and-paste data augmentation pipeline for structured 3D point cloud scenes, named CorrelaBoost. Observing that 3D objects should have reasonable relative positions in a structured scene because of the objects’ functionalities and natural relationships, we express this correlation as a kind of interactive force. An energy field called Correlation Field can be calculated correspondingly across the whole 3D space. According to the Correlation Field, we propose two data augmentation strategies to explore highly congruent positions that a designated object may be pasted to: 1) Category Consistent Exchanging and 2) Energy Optimized Transformation. We conduct exhaustive experiments on various popular benchmarks with different detection frameworks and the results illustrate that our method brings huge free-lunch improvement and significantly outperforms state-of-the-art approaches in terms of data augmentation. It is worth noting that the performance of VoteNet with mAP@0. 5 is improved by 7. 7 on ScanNetV2 dataset and 5. 0 on SUN RGB-D dataset. Our method is simple to implement and increases few computational overhead.

PDF Details

AAAI Conference 2022 Conference Paper

Unsupervised Representation for Semantic Segmentation by Implicit Cycle-Attention Contrastive Learning

Bo Pang
Yizhuo Li
Yifan Zhang
Gao Peng
Jiajun Tang
Kaiwen Zha
Jiefeng Li
Cewu Lu

We study the unsupervised representation learning for the semantic segmentation task. Different from previous works that aim at providing unsupervised pre-trained backbones for segmentation models which need further supervised fine-tune, here, we focus on providing representation that is only trained by unsupervised methods. This means models need to directly generate pixel-level, linearly separable semantic results. We first explore and present two factors that have significant effects on segmentation under the contrastive learning framework: 1) the difficulty and diversity of the positive contrastive pairs, 2) the balance of global and local features. With the intention of optimizing these factors, we propose the cycle-attention contrastive learning (CACL). CACL makes use of semantic continuity of video frames, adopting unsupervised cycle-consistent attention mechanism to implicitly conduct contrastive learning with difficult, global-local-balanced positive pixel pairs. Compared with baseline model MoCo-v2 and other unsupervised methods, CACL demonstrates consistently superior performance on PASCAL VOC (+4. 5 mIoU) and Cityscapes (+4. 5 mIoU) datasets.

PDF Details

NeurIPS Conference 2021 Conference Paper

Localization with Sampling-Argmax

Jiefeng Li
Tong Chen
Ruiqi Shi
Yujing Lou
Yong-Lu Li
Cewu Lu

Soft-argmax operation is commonly adopted in detection-based methods to localize the target position in a differentiable manner. However, training the neural network with soft-argmax makes the shape of the probability map unconstrained. Consequently, the model lacks pixel-wise supervision through the map during training, leading to performance degradation. In this work, we propose sampling-argmax, a differentiable training method that imposes implicit constraints to the shape of the probability map by minimizing the expectation of the localization error. To approximate the expectation, we introduce a continuous formulation of the output distribution and develop a differentiable sampling process. The expectation can be approximated by calculating the average error of all samples drawn from the output distribution. We show that sampling-argmax can seamlessly replace the conventional soft-argmax operation on various localization tasks. Comprehensive experiments demonstrate the effectiveness and flexibility of the proposed method. Code is available at https: //github. com/Jeff-sjtu/sampling-argmax

PDF Details

AAAI Conference 2021 Conference Paper

TDAF: Top-Down Attention Framework for Vision Tasks

Bo Pang
Yizhuo Li
Jiefeng Li
Muchen Li
Hanwen Cao
Cewu Lu

Human attention mechanisms often work in a top-down manner, yet it is not well explored in vision research. Here, we propose the Top-Down Attention Framework (TDAF) to capture top-down attentions, which can be easily adopted in most existing models. The designed Recursive Dual-Directional Nested Structure in it forms two sets of orthogonal paths, recursive and structural ones, where bottom-up spatial features and top-down attention features are extracted respectively. Such spatial and attention features are nested deeply, therefore, the proposed framework works in a mixed top-down and bottom-up manner. Empirical evidence shows that our TDAF can capture effective stratified attention information and boost performance. ResNet with TDAF achieves 2. 0% improvements on ImageNet. For object detection, the performance is improved by 2. 7% AP over FCOS. For pose estimation, TDAF improves the baseline by 1. 6%. And for action recognition, the 3D-ResNet adopting TDAF achieves improvements of 1. 7% accuracy.

PDF Details