Search Results for author: Zhengkai Jiang

Found 24 papers, 15 papers with code

Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering

no code implementations13 Dec 2018 Gao Peng, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven Hoi, Xiaogang Wang, Hongsheng Li

It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering.

Question Answering Visual Question Answering

Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

3 code implementations26 Aug 2019 Benjin Zhu, Zhengkai Jiang, Xiangxin Zhou, Zeming Li, Gang Yu

This report presents our method which wins the nuScenes3D Detection Challenge [17] held in Workshop on Autonomous Driving(WAD, CVPR 2019).

3D Object Detection Autonomous Driving +1

Learning Where to Focus for Efficient Video Object Detection

1 code implementation ECCV 2020 Zhengkai Jiang, Yu Liu, Ceyuan Yang, Jihao Liu, Peng Gao, Qian Zhang, Shiming Xiang, Chunhong Pan

Transferring existing image-based detectors to the video is non-trivial since the quality of frames is always deteriorated by part occlusion, rare pose, and motion blur.

Object object-detection +1

AutoAssign: Differentiable Label Assignment for Dense Object Detection

2 code implementations7 Jul 2020 Benjin Zhu, Jian-Feng Wang, Zhengkai Jiang, Fuhang Zong, Songtao Liu, Zeming Li, Jian Sun

During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions.

Dense Object Detection Object +1

Contrastive Visual-Linguistic Pretraining

no code implementations26 Jul 2020 Lei Shi, Kai Shuang, Shijie Geng, Peng Su, Zhengkai Jiang, Peng Gao, Zuohui Fu, Gerard de Melo, Sen Su

We evaluate CVLP on several down-stream tasks, including VQA, GQA and NLVR2 to validate the superiority of contrastive learning on multi-modality representation learning.

Contrastive Learning regression +2

Fine-Grained Dynamic Head for Object Detection

1 code implementation NeurIPS 2020 Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Hongbin Sun, Jian Sun, Nanning Zheng

To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level combination of FPN features from different scales for each instance, which further releases the ability of multi-scale feature representation.

Object object-detection +1

SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking

no code implementations24 May 2021 Jinlong Peng, Zhengkai Jiang, Yueyang Gu, Yang Wu, Yabiao Wang, Ying Tai, Chengjie Wang, Weiyao Lin

In addition, we add a localization branch to predict the localization accuracy, so that it can work as the replacement of the regression assistance link during inference.

Classification Object +2

You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction

1 code implementation30 May 2022 Ziteng Cui, Kunchang Li, Lin Gu, Shenghan Su, Peng Gao, Zhengkai Jiang, Yu Qiao, Tatsuya Harada

Challenging illumination conditions (low-light, under-exposure and over-exposure) in the real world not only cast an unpleasant visual appearance but also taint the computer vision tasks.

Low-Light Image Enhancement object-detection +2

Rethinking Mobile Block for Efficient Attention-based Models

1 code implementation ICCV 2023 Jiangning Zhang, Xiangtai Li, Jian Li, Liang Liu, Zhucun Xue, Boshen Zhang, Zhengkai Jiang, Tianxin Huang, Yabiao Wang, Chengjie Wang

This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance.

Unity

Personalize Segment Anything Model with One Shot

1 code implementation4 May 2023 Renrui Zhang, Zhengkai Jiang, Ziyu Guo, Shilin Yan, Junting Pan, Xianzheng Ma, Hao Dong, Peng Gao, Hongsheng Li

Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models.

Personalized Segmentation Segmentation +4

Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model

1 code implementation18 May 2023 Siyuan Huang, Zhengkai Jiang, Hao Dong, Yu Qiao, Peng Gao, Hongsheng Li

This paper presents Instruct2Act, a framework that utilizes Large Language Models to map multi-modal instructions to sequential actions for robotic manipulation tasks.

Language Modelling Large Language Model +2

Dual Path Transformer with Partition Attention

no code implementations24 May 2023 Zhengkai Jiang, Liang Liu, Jiangning Zhang, Yabiao Wang, Mingang Chen, Chengjie Wang

This paper introduces a novel attention mechanism, called dual attention, which is both efficient and effective.

Image Classification object-detection +2

Density Matters: Improved Core-set for Active Domain Adaptive Segmentation

no code implementations15 Dec 2023 Shizhan Liu, Zhengkai Jiang, Yuxi Li, Jinlong Peng, Yabiao Wang, Weiyao Lin

Active domain adaptation has emerged as a solution to balance the expensive annotation cost and the performance of trained models in semantic segmentation.

Domain Adaptation Semantic Segmentation

DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation

no code implementations10 Mar 2024 Xiaobin Hu, Xu Peng, Donghao Luo, Xiaozhong Ji, Jinlong Peng, Zhengkai Jiang, Jiangning Zhang, Taisong Jin, Chengjie Wang, Rongrong Ji

Our DiffuMatting shows several potential applications (e. g., matting-data generator, community-friendly art design and controllable generation).

Image Matting Object

Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection

no code implementations18 Mar 2024 Liren He, Zhengkai Jiang, Jinlong Peng, Liang Liu, Qiangang Du, Xiaobin Hu, Wenbing Zhu, Mingmin Chi, Yabiao Wang, Chengjie Wang

In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of ``learning shortcuts'', wherein the model fails to learn the patterns of normal samples as it should, opting instead for shortcuts such as identity mapping or artificial noise elimination.

Anomaly Detection

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