Search Results for author: Chao Qi

Found 8 papers, 2 papers with code

Towards Physically-Realizable Adversarial Attacks in Embodied Vision Navigation

1 code implementation16 Sep 2024 Meng Chen, Jiawei Tu, Chao Qi, Yonghao Dang, Feng Zhou, Wei Wei, Jianqin Yin

Experimental results show our adversarial patches reduce navigation success rates by about 40%, outperforming previous methods in practicality, effectiveness, and naturalness.

object-detection Object Detection

Micro-expression recognition based on depth map to point cloud

no code implementations12 Jun 2024 Ren Zhang, Jianqin Yin, Chao Qi, Zehao Wang, Zhicheng Zhang, Yonghao Dang

Conversely, depth information can effectively represent motion information related to facial structure changes and is not affected by lighting.

Micro Expression Recognition Micro-Expression Recognition

Instance-incremental Scene Graph Generation from Real-world Point Clouds via Normalizing Flows

no code implementations21 Feb 2023 Chao Qi, Jianqin Yin, Jinghang Xu, Pengxiang Ding

This work introduces a new task of instance-incremental scene graph generation: Given a scene of the point cloud, representing it as a graph and automatically increasing novel instances.

Graph Generation Scene Graph Generation

Neighborhood Spatial Aggregation MC Dropout for Efficient Uncertainty-aware Semantic Segmentation in Point Clouds

no code implementations5 Dec 2021 Chao Qi, Jianqin Yin

Specifically, the NSA-MC dropout samples the model many times through a space-dependent way, outputting point-wise distribution by aggregating stochastic inference results of neighbors.

Model Optimization Semantic Segmentation

Tea Chrysanthemum Detection under Unstructured Environments Using the TC-YOLO Model

no code implementations4 Nov 2021 Chao Qi, Junfeng Gao, Simon Pearson, Helen Harman, Kunjie Chen, Lei Shu

Tea chrysanthemum detection at its flowering stage is one of the key components for selective chrysanthemum harvesting robot development.

Neural Response Generation via GAN with an Approximate Embedding Layer

no code implementations EMNLP 2017 Zhen Xu, Bingquan Liu, Baoxun Wang, Chengjie Sun, Xiaolong Wang, Zhuoran Wang, Chao Qi

This paper presents a Generative Adversarial Network (GAN) to model single-turn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machine-generated ones.

Diversity Generative Adversarial Network +2

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