Search Results for author: Chaohui Yu

Found 18 papers, 4 papers with code

SingleInsert: Inserting New Concepts from a Single Image into Text-to-Image Models for Flexible Editing

no code implementations12 Oct 2023 Zijie Wu, Chaohui Yu, Zhen Zhu, Fan Wang, Xiang Bai

To utilize the abundant visual priors in the off-the-shelf T2I models, a series of methods try to invert an image to proper embedding that aligns with the semantic space of the T2I model.

Image Generation Novel View Synthesis

ES-MVSNet: Efficient Framework for End-to-end Self-supervised Multi-View Stereo

no code implementations4 Aug 2023 Qiang Zhou, Chaohui Yu, Jingliang Li, Yuang Liu, Jing Wang, Zhibin Wang

to provide additional consistency constraints, which grows GPU memory consumption and complicates the model's structure and training pipeline.

Optical Flow Estimation Semantic Segmentation

RegionBLIP: A Unified Multi-modal Pre-training Framework for Holistic and Regional Comprehension

1 code implementation3 Aug 2023 Qiang Zhou, Chaohui Yu, Shaofeng Zhang, Sitong Wu, Zhibing Wang, Fan Wang

To this end, we propose to extract features corresponding to regional objects as soft prompts for LLM, which provides a straightforward and scalable approach and eliminates the need for LLM fine-tuning.

Image Comprehension

Improved Neural Radiance Fields Using Pseudo-depth and Fusion

no code implementations27 Jul 2023 Jingliang Li, Qiang Zhou, Chaohui Yu, Zhengda Lu, Jun Xiao, Zhibin Wang, Fan Wang

To make the constructed volumes as close as possible to the surfaces of objects in the scene and the rendered depth more accurate, we propose to perform depth prediction and radiance field reconstruction simultaneously.

Depth Estimation Depth Prediction +1

Points-to-3D: Bridging the Gap between Sparse Points and Shape-Controllable Text-to-3D Generation

no code implementations26 Jul 2023 Chaohui Yu, Qiang Zhou, Jingliang Li, Zhe Zhang, Zhibin Wang, Fan Wang

To better utilize the sparse 3D points, we propose an efficient point cloud guidance loss to adaptively drive the NeRF's geometry to align with the shape of the sparse 3D points.

Text to 3D

D2Q-DETR: Decoupling and Dynamic Queries for Oriented Object Detection with Transformers

no code implementations1 Mar 2023 Qiang Zhou, Chaohui Yu, Zhibin Wang, Fan Wang

In this paper, we propose an end-to-end framework for oriented object detection, which simplifies the model pipeline and obtains superior performance.

Object object-detection +3

LMSeg: Language-guided Multi-dataset Segmentation

no code implementations27 Feb 2023 Qiang Zhou, Yuang Liu, Chaohui Yu, Jingliang Li, Zhibin Wang, Fan Wang

Instead of relabeling each dataset with the unified taxonomy, a category-guided decoding module is designed to dynamically guide predictions to each datasets taxonomy.

Image Augmentation Panoptic Segmentation +1

MimCo: Masked Image Modeling Pre-training with Contrastive Teacher

no code implementations7 Sep 2022 Qiang Zhou, Chaohui Yu, Hao Luo, Zhibin Wang, Hao Li

Specifically, MimCo takes a pre-trained contrastive learning model as the teacher model and is pre-trained with two types of learning targets: patch-level and image-level reconstruction losses.

Contrastive Learning Self-Supervised Learning

Point RCNN: An Angle-Free Framework for Rotated Object Detection

no code implementations28 May 2022 Qiang Zhou, Chaohui Yu, Zhibin Wang, Hao Li

To tackle this problem, we propose a purely angle-free framework for rotated object detection, called Point RCNN, which mainly consists of PointRPN and PointReg.

Object object-detection +1

Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework

1 code implementation CVPR 2021 Qiang Zhou, Chaohui Yu, Zhibin Wang, Qi Qian, Hao Li

To alleviate the confirmation bias problem and improve the quality of pseudo annotations, we further propose a co-rectify scheme based on Instant-Teaching, denoted as Instant-Teaching$^*$.

Ranked #12 on Semi-Supervised Object Detection on COCO 100% labeled data (using extra training data)

Object object-detection +2

Learning Invariant Representations across Domains and Tasks

no code implementations3 Mar 2021 Jindong Wang, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, Tie-Yan Liu

Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledge from the abundant typical pneumonia datasets for COVID-19 image classification.

Domain Adaptation Image Classification +1

Object Detection Made Simpler by Eliminating Heuristic NMS

no code implementations28 Jan 2021 Qiang Zhou, Chaohui Yu, Chunhua Shen, Zhibin Wang, Hao Li

On the COCO dataset, our simple design achieves superior performance compared to both the FCOS baseline detector with NMS post-processing and the recent end-to-end NMS-free detectors.

Object object-detection +1

Learning to Match Distributions for Domain Adaptation

1 code implementation17 Jul 2020 Chaohui Yu, Jindong Wang, Chang Liu, Tao Qin, Renjun Xu, Wenjie Feng, Yiqiang Chen, Tie-Yan Liu

However, it remains challenging to determine which method is suitable for a given application since they are built with certain priors or bias.

Domain Adaptation Inductive Bias

Transfer Learning with Dynamic Adversarial Adaptation Network

no code implementations18 Sep 2019 Chaohui Yu, Jindong Wang, Yiqiang Chen, Meiyu Huang

In this paper, we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically learn domain-invariant representations while quantitatively evaluate the relative importance of global and local domain distributions.

Domain Adaptation Transfer Learning

Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning

1 code implementation25 Mar 2019 Chaohui Yu, Jindong Wang, Yiqiang Chen, Zijing Wu

In this paper, we propose a unified Transfer Channel Pruning (TCP) approach for accelerating UDA models.

Transfer Learning Unsupervised Domain Adaptation

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