322 citations · 778 across the 16 of their papers we have counts for
16 papers
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection
Tianwu Lei, Silin Chen, Bohan Wang +2
Most unsupervised anomaly detection methods based on representations of normal samples to distinguish anomalies have recently made remarkable progress. However, existing methods on…
Dual Path Transformer with Partition Attention
Zhengkai Jiang, Liang Liu, Jiangning Zhang +3
This paper introduces a novel attention mechanism, called dual attention, which is both efficient and effective. The dual attention mechanism consists of two parallel components: l…
Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model
Siyuan Huang, Zhengkai Jiang, Hao Dong +3
Foundation models have made significant strides in various applications, including text-to-image generation, panoptic segmentation, and natural language processing. This paper pres…
Personalize Segment Anything Model with One Shot
Renrui Zhang, Zhengkai Jiang, Ziyu Guo +6
Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models. Despite the g…
Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation
Zhengkai Jiang, Yuxi Li, Ceyuan Yang +4
Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Ada…
You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction
Ziteng Cui, Kunchang Li, Lin Gu +5
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…
STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation
Zhengkai Jiang, Zhangxuan Gu, Jinlong Peng +6
Video Instance Segmentation (VIS) is a task that simultaneously requires classification, segmentation, and instance association in a video. Recent VIS approaches rely on sophistica…
Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework
Qingyu Song, Changan Wang, Zhengkai Jiang +6
Localizing individuals in crowds is more in accordance with the practical demands of subsequent high-level crowd analysis tasks than simply counting. However, existing localization…
SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking
Jinlong Peng, Zhengkai Jiang, Yueyang Gu +5
Recently, most siamese network based trackers locate targets via object classification and bounding-box regression. Generally, they select the bounding-box with maximum classificat…
Fine-Grained Dynamic Head for Object Detection
Lin Song, Yanwei Li, Zhengkai Jiang +4
The Feature Pyramid Network (FPN) presents a remarkable approach to alleviate the scale variance in object representation by performing instance-level assignments. Nevertheless, th…
Rethinking Learnable Tree Filter for Generic Feature Transform
Lin Song, Yanwei Li, Zhengkai Jiang +5
The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation. Nevertheless, the intrinsic geometric constraint forces…
Contrastive Visual-Linguistic Pretraining
Lei Shi, Kai Shuang, Shijie Geng +6
Several multi-modality representation learning approaches such as LXMERT and ViLBERT have been proposed recently. Such approaches can achieve superior performance due to the high-l…
AutoAssign: Differentiable Label Assignment for Dense Object Detection
Benjin Zhu, Jianfeng Wang, Zhengkai Jiang +4
Determining positive/negative samples for object detection is known as label assignment. Here we present an anchor-free detector named AutoAssign. It requires little human knowledg…
Learning Where to Focus for Efficient Video Object Detection
Zhengkai Jiang, Yu Liu, Ceyuan Yang +5
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. Previous a…
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
Benjin Zhu, Zhengkai Jiang, Xiangxin Zhou +2
This report presents our method which wins the nuScenes3D Detection Challenge [17] held in Workshop on Autonomous Driving(WAD, CVPR 2019). Generally, we utilize sparse 3D convoluti…
Dynamic Fusion with Intra- and Inter- Modality Attention Flow for Visual Question Answering
Gao Peng, Zhengkai Jiang, Haoxuan You +4
Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fusing multi-modal features with intra-…