Search Results for author: YaLi Li

Found 21 papers, 9 papers with code

Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey

no code implementations4 Mar 2024 Lingyan Ran, YaLi Li, Guoqiang Liang, Yanning Zhang

Semantic segmentation is an important and popular research area in computer vision that focuses on classifying pixels in an image based on their semantics.

Image Segmentation Pseudo Label +2

Uni3DETR: Unified 3D Detection Transformer

1 code implementation NeurIPS 2023 Zhenyu Wang, YaLi Li, Xi Chen, Hengshuang Zhao, Shengjin Wang

In this paper, we propose Uni3DETR, a unified 3D detector that addresses indoor and outdoor 3D detection within the same framework.

Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval

1 code implementation24 Oct 2022 Zhaopeng Dou, Zhongdao Wang, Weihua Chen, YaLi Li, Shengjin Wang

(3) the data uncertainty and the model uncertainty are jointly learned in a unified network, and they serve as two fundamental criteria for the reliability assessment: if a probe is high-quality (low data uncertainty) and the model is confident in the prediction of the probe (low model uncertainty), the final ranking will be assessed as reliable.

Image Retrieval Retrieval

Self-Supervised Learning via Maximum Entropy Coding

1 code implementation20 Oct 2022 Xin Liu, Zhongdao Wang, YaLi Li, Shengjin Wang

To cope with this issue, we propose Maximum Entropy Coding (MEC), a more principled objective that explicitly optimizes on the structure of the representation, so that the learned representation is less biased and thus generalizes better to unseen downstream tasks.

Instance Segmentation object-detection +4

GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs

1 code implementation19 Oct 2022 Xin Liu, Xiaofei Shao, Bo wang, YaLi Li, Shengjin Wang

First, unlike previous methods, we leverage convolution neural networks as well as graph neural networks in a complementary way for geometric representation learning.

Autonomous Driving Depth Completion +1

Portrait Interpretation and a Benchmark

no code implementations27 Jul 2022 Yixuan Fan, Zhaopeng Dou, YaLi Li, Shengjin Wang

Furthermore, we focus on representation learning for portrait interpretation and propose a baseline that reflects our systematic perspective.

Attribute Multi-Task Learning +2

Hybrid Physical Metric For 6-DoF Grasp Pose Detection

1 code implementation22 Jun 2022 Yuhao Lu, Beixing Deng, Zhenyu Wang, Peiyuan Zhi, YaLi Li, Shengjin Wang

6-DoF grasp pose detection of multi-grasp and multi-object is a challenge task in the field of intelligent robot.

R(Det)2: Randomized Decision Routing for Object Detection

no code implementations CVPR 2022 YaLi Li, Shengjin Wang

In this paper, we propose a novel approach to combine decision trees and deep neural networks in an end-to-end learning manner for object detection.

Object object-detection +1

OSKDet: Orientation-Sensitive Keypoint Localization for Rotated Object Detection

no code implementations CVPR 2022 Dongchen Lu, Dongmei Li, YaLi Li, Shengjin Wang

By proposing the orientation-sensitive heatmap, OSKDet could learn the shape and direction of rotated target implicitly and has stronger modeling capabilities for rotated representation, which improves the localization accuracy and acquires high quality detection results.

Object object-detection +2

Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation

1 code implementation20 Jul 2021 Qinglin Zhang, Qian Chen, YaLi Li, Jiaqing Liu, Wen Wang

Evaluations are conducted on the English Wiki-727K document segmentation benchmark, a Chinese Wikipedia-based document segmentation dataset we created, and an in-house Chinese spoken document dataset.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

AdaZoom: Adaptive Zoom Network for Multi-Scale Object Detection in Large Scenes

no code implementations19 Jun 2021 Jingtao Xu, YaLi Li, Shengjin Wang

In this paper, we propose a novel Adaptive Zoom (AdaZoom) network as a selective magnifier with flexible shape and focal length to adaptively zoom the focus regions for object detection.

object-detection Object Detection

Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection

no code implementations CVPR 2021 Zhenyu Wang, YaLi Li, Ye Guo, Lu Fang, Shengjin Wang

In this paper, we delve into semi-supervised object detection where unlabeled images are leveraged to break through the upper bound of fully-supervised object detection models.

Object object-detection +2

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