Search Results for author: Minghao Chen

Found 26 papers, 18 papers with code

Underwater Acoustic Signal Recognition Based on Salient Feature

no code implementations20 Dec 2023 Minghao Chen

The proposed approach involves continual learning of features extracted from spectra for the classification of underwater acoustic signals.

Continual Learning

SHAP-EDITOR: Instruction-guided Latent 3D Editing in Seconds

no code implementations14 Dec 2023 Minghao Chen, Junyu Xie, Iro Laina, Andrea Vedaldi

In particular, we hypothesise that editing can be greatly simplified by first encoding 3D objects in a suitable latent space.

PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer

1 code implementation CVPR 2023 Honghui Yang, Wenxiao Wang, Minghao Chen, Binbin Lin, Tong He, Hua Chen, Xiaofei He, Wanli Ouyang

The key to associating the two different representations is our introduced input-dependent Query Initialization module, which could efficiently generate reference points and content queries.

Autonomous Driving Quantization

Training-Free Layout Control with Cross-Attention Guidance

1 code implementation6 Apr 2023 Minghao Chen, Iro Laina, Andrea Vedaldi

We thoroughly evaluate our approach on three benchmarks and provide several qualitative examples and a comparative analysis of the two strategies that demonstrate the superiority of backward guidance compared to forward guidance, as well as prior work.

Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations

no code implementations6 Dec 2022 Minghao Chen, Renbo Tu, Chenxi Huang, Yuqi Lin, Boxi Wu, Deng Cai

In this paper, we introduce a new framework of contrastive action representation learning (CARL) to learn frame-wise action representation in a self-supervised or weakly-supervised manner, especially for long videos.

Action Classification Contrastive Learning +4

Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning

1 code implementation CVPR 2022 Minghao Chen, Fangyun Wei, Chong Li, Deng Cai

In this paper, we introduce a novel contrastive action representation learning (CARL) framework to learn frame-wise action representations, especially for long videos, in a self-supervised manner.

Action Classification Contrastive Learning +4

Searching the Search Space of Vision Transformer

1 code implementation NeurIPS 2021 Minghao Chen, Kan Wu, Bolin Ni, Houwen Peng, Bei Liu, Jianlong Fu, Hongyang Chao, Haibin Ling

Vision Transformer has shown great visual representation power in substantial vision tasks such as recognition and detection, and thus been attracting fast-growing efforts on manually designing more effective architectures.

Neural Architecture Search object-detection +4

AutoFormer: Searching Transformers for Visual Recognition

2 code implementations ICCV 2021 Minghao Chen, Houwen Peng, Jianlong Fu, Haibin Ling

Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained from scratch.

AutoML Fine-Grained Image Classification

CRFL: Certifiably Robust Federated Learning against Backdoor Attacks

1 code implementation15 Jun 2021 Chulin Xie, Minghao Chen, Pin-Yu Chen, Bo Li

Our method exploits clipping and smoothing on model parameters to control the global model smoothness, which yields a sample-wise robustness certification on backdoors with limited magnitude.

Federated Learning

Salient Object Ranking with Position-Preserved Attention

1 code implementation ICCV 2021 Hao Fang, Daoxin Zhang, Yi Zhang, Minghao Chen, Jiawei Li, Yao Hu, Deng Cai, Xiaofei He

In this paper, we study the Salient Object Ranking (SOR) task, which manages to assign a ranking order of each detected object according to its visual saliency.

Image Cropping Instance Segmentation +7

One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking

1 code implementation CVPR 2021 Minghao Chen, Houwen Peng, Jianlong Fu, Haibin Ling

In this paper, we propose a one-shot neural ensemble architecture search (NEAS) solution that addresses the two challenges.

Neural Architecture Search

Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification

no code implementations29 Jan 2021 Hao Feng, Minghao Chen, Jinming Hu, Dong Shen, Haifeng Liu, Deng Cai

In this paper, to complement these low recall neighbor pseudo labels, we propose a joint learning framework to learn better feature embeddings via high precision neighbor pseudo labels and high recall group pseudo labels.

Person Re-Identification Unsupervised Domain Adaptation

SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations

3 code implementations21 Nov 2020 Hao-Zhe Feng, Kezhi Kong, Minghao Chen, Tianye Zhang, Minfeng Zhu, Wei Chen

Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results.

4k Semi-Supervised Image Classification +1

Reducing the Teacher-Student Gap via Spherical Knowledge Disitllation

1 code implementation15 Oct 2020 Jia Guo, Minghao Chen, Yao Hu, Chen Zhu, Xiaofei He, Deng Cai

We investigate this problem by study the gap of confidence between teacher and student.

Knowledge Distillation

Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework

no code implementations10 Oct 2020 Wenxiao Wang, Minghao Chen, Shuai Zhao, Long Chen, Jinming Hu, Haifeng Liu, Deng Cai, Xiaofei He, Wei Liu

Specifically, it first casts the relationships between a certain model's accuracy and depth/width/resolution into a polynomial regression and then maximizes the polynomial to acquire the optimal values for the three dimensions.

Network Pruning Neural Architecture Search +1

Adversarial-Learned Loss for Domain Adaptation

1 code implementation4 Jan 2020 Minghao Chen, Shuai Zhao, Haifeng Liu, Deng Cai

In order to combine the strengths of these two methods, we propose a novel method called Adversarial-Learned Loss for Domain Adaptation (ALDA).

Domain Adaptation Pseudo Label

DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration

no code implementations21 Dec 2019 Wenxiao Wang, Shuai Zhao, Minghao Chen, Jinming Hu, Deng Cai, Haifeng Liu

The dominant pruning methods, filter-level pruning methods, evaluate their performance through the reduction ratio of computations and deem that a higher reduction ratio of computations is equivalent to a higher acceleration ratio in terms of inference time.

Network Pruning

Domain Adaptation for Semantic Segmentation with Maximum Squares Loss

1 code implementation ICCV 2019 Minghao Chen, Hongyang Xue, Deng Cai

However, when applying the entropy minimization to UDA for semantic segmentation, the gradient of the entropy is biased towards samples that are easy to transfer.

Semantic Segmentation Unsupervised Domain Adaptation

Learning color space adaptation from synthetic to real images of cirrus clouds

no code implementations24 Oct 2018 Qing Lyu, Minghao Chen, Xiang Chen

With our adapted synthetic data for training the semantic segmentation, we achieve an improvement of 6:59% when applied to real images, superior to alternative methods.

Segmentation Semantic Segmentation

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