Search Results for author: Yinpeng Chen

Found 51 papers, 23 papers with code

Efficient Modulation for Vision Networks

1 code implementation29 Mar 2024 Xu Ma, Xiyang Dai, Jianwei Yang, Bin Xiao, Yinpeng Chen, Yun Fu, Lu Yuan

We demonstrate that the modulation mechanism is particularly well suited for efficient networks and further tailor the modulation design by proposing the efficient modulation (EfficientMod) block, which is considered the essential building block for our networks.

On the Hidden Waves of Image

no code implementations19 Oct 2023 Yinpeng Chen, Dongdong Chen, Xiyang Dai, Mengchen Liu, Lu Yuan, Zicheng Liu, Youzuo Lin

We term this phenomenon hidden waves, as it reveals that, although the speeds of the set of wave equations and autoregressive coefficient matrices are latent, they are both learnable and shared across images.

Completing Visual Objects via Bridging Generation and Segmentation

no code implementations1 Oct 2023 Xiang Li, Yinpeng Chen, Chung-Ching Lin, Hao Chen, Kai Hu, Rita Singh, Bhiksha Raj, Lijuan Wang, Zicheng Liu

This paper presents a novel approach to object completion, with the primary goal of reconstructing a complete object from its partially visible components.

Image Generation Object +1

An Empirical Study of Large-Scale Data-Driven Full Waveform Inversion

no code implementations28 Jul 2023 Peng Jin, Yinan Feng, Shihang Feng, Hanchen Wang, Yinpeng Chen, Benjamin Consolvo, Zicheng Liu, Youzuo Lin

This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem.


Designing a Better Asymmetric VQGAN for StableDiffusion

2 code implementations7 Jun 2023 Zixin Zhu, Xuelu Feng, Dongdong Chen, Jianmin Bao, Le Wang, Yinpeng Chen, Lu Yuan, Gang Hua

The training cost of our asymmetric VQGAN is cheap, and we only need to retrain a new asymmetric decoder while keeping the vanilla VQGAN encoder and StableDiffusion unchanged.

Decoder Image Inpainting

Image as First-Order Norm+Linear Autoregression: Unveiling Mathematical Invariance

no code implementations25 May 2023 Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Lu Yuan, Zicheng Liu, Youzuo Lin

This paper introduces a novel mathematical property applicable to diverse images, referred to as FINOLA (First-Order Norm+Linear Autoregressive).

Decoder Image Classification +4

Auto-Linear Phenomenon in Subsurface Imaging

no code implementations27 Apr 2023 Yinan Feng, Yinpeng Chen, Peng Jin, Shihang Feng, Zicheng Liu, Youzuo Lin

Subsurface imaging involves solving full waveform inversion (FWI) to predict geophysical properties from measurements.

Decoder Geophysics +2

Find Beauty in the Rare: Contrastive Composition Feature Clustering for Nontrivial Cropping Box Regression

no code implementations17 Feb 2023 Zhiyu Pan, Yinpeng Chen, Jiale Zhang, Hao Lu, Zhiguo Cao, Weicai Zhong

Observing that similar composition patterns tend to be shared by the cropping boundaries annotated nearly, we argue to find the beauty of composition from the rare samples by clustering the samples with similar cropping boundary annotations, ie, similar composition patterns.

Clustering Image Cropping +2

Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning

4 code implementations CVPR 2023 Rui Wang, Dongdong Chen, Zuxuan Wu, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Lu Yuan, Yu-Gang Jiang

For the choice of teacher models, we observe that students taught by video teachers perform better on temporally-heavy video tasks, while image teachers transfer stronger spatial representations for spatially-heavy video tasks.

Action Classification Representation Learning +1

Self-Supervised Learning based on Heat Equation

no code implementations23 Nov 2022 Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Lu Yuan, Zicheng Liu, Youzuo Lin

When transferring to object detection with frozen backbone, QB-Heat outperforms MoCo-v2 and supervised pre-training on ImageNet by 7. 9 and 4. 5 AP respectively.

Image Classification object-detection +2

Video Mobile-Former: Video Recognition with Efficient Global Spatial-temporal Modeling

no code implementations25 Aug 2022 Rui Wang, Zuxuan Wu, Dongdong Chen, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Luowei Zhou, Lu Yuan, Yu-Gang Jiang

To avoid significant computational cost incurred by computing self-attention between the large number of local patches in videos, we propose to use very few global tokens (e. g., 6) for a whole video in Transformers to exchange information with 3D-CNNs with a cross-attention mechanism.

Video Recognition

Design What You Desire: Icon Generation from Orthogonal Application and Theme Labels

1 code implementation31 Jul 2022 Yinpeng Chen, Zhiyu Pan, Min Shi, Hao Lu, Zhiguo Cao, Weicai Zhong

Generative adversarial networks (GANs) have been trained to be professional artists able to create stunning artworks such as face generation and image style transfer.

Disentanglement Face Generation +1

Should All Proposals be Treated Equally in Object Detection?

1 code implementation7 Jul 2022 Yunsheng Li, Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Pei Yu, Jing Yin, Lu Yuan, Zicheng Liu, Nuno Vasconcelos

We formulate this as a learning problem where the goal is to assign operators to proposals, in the detection head, so that the total computational cost is constrained and the precision is maximized.

Object Object Detection

An Intriguing Property of Geophysics Inversion

no code implementations28 Apr 2022 Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu, Youzuo Lin

In particular, when dealing with the inversion from seismic data to subsurface velocity governed by a wave equation, the integral results of velocity with Gaussian kernels are linearly correlated to the integral of seismic data with sine kernels.

Decoder Geophysics

Residual Mixture of Experts

no code implementations20 Apr 2022 Lemeng Wu, Mengchen Liu, Yinpeng Chen, Dongdong Chen, Xiyang Dai, Lu Yuan

In this paper, we propose Residual Mixture of Experts (RMoE), an efficient training pipeline for MoE vision transformers on downstream tasks, such as segmentation and detection.

object-detection Object Detection

The Overlooked Classifier in Human-Object Interaction Recognition

no code implementations10 Mar 2022 Ying Jin, Yinpeng Chen, Lijuan Wang, JianFeng Wang, Pei Yu, Lin Liang, Jenq-Neng Hwang, Zicheng Liu

Human-Object Interaction (HOI) recognition is challenging due to two factors: (1) significant imbalance across classes and (2) requiring multiple labels per image.

Classification Human-Object Interaction Detection +4

Extremely Weak Supervision Inversion of Multi-physical Properties

no code implementations3 Feb 2022 Shihang Feng, Peng Jin, Xitong Zhang, Yinpeng Chen, David Alumbaugh, Michael Commer, Youzuo Lin

We explore a multi-physics inversion problem from two distinct measurements~(seismic and EM data) to three geophysical properties~(velocity, conductivity, and CO$_2$ saturation).


Improving Vision Transformers for Incremental Learning

no code implementations12 Dec 2021 Pei Yu, Yinpeng Chen, Ying Jin, Zicheng Liu

This paper proposes a working recipe of using Vision Transformer (ViT) in class incremental learning.

Class Incremental Learning Incremental Learning

BEVT: BERT Pretraining of Video Transformers

1 code implementation CVPR 2022 Rui Wang, Dongdong Chen, Zuxuan Wu, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Yu-Gang Jiang, Luowei Zhou, Lu Yuan

This design is motivated by two observations: 1) transformers learned on image datasets provide decent spatial priors that can ease the learning of video transformers, which are often times computationally-intensive if trained from scratch; 2) discriminative clues, i. e., spatial and temporal information, needed to make correct predictions vary among different videos due to large intra-class and inter-class variations.

Action Recognition Representation Learning

OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion

2 code implementations4 Nov 2021 Chengyuan Deng, Shihang Feng, Hanchen Wang, Xitong Zhang, Peng Jin, Yinan Feng, Qili Zeng, Yinpeng Chen, Youzuo Lin

The recent success of data-driven FWI methods results in a rapidly increasing demand for open datasets to serve the geophysics community.

2k Benchmarking +2

Unsupervised Finetuning

no code implementations18 Oct 2021 Suichan Li, Dongdong Chen, Yinpeng Chen, Lu Yuan, Lei Zhang, Qi Chu, Bin Liu, Nenghai Yu

This problem is more challenging than the supervised counterpart, as the low data density in the small-scale target data is not friendly for unsupervised learning, leading to the damage of the pretrained representation and poor representation in the target domain.

Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop

no code implementations ICLR 2022 Peng Jin, Xitong Zhang, Yinpeng Chen, Sharon Xiaolei Huang, Zicheng Liu, Youzuo Lin

In particular, we use finite difference to approximate the forward modeling of PDE as a differentiable operator (from velocity map to seismic data) and model its inversion by CNN (from seismic data to velocity map).


MicroNet: Improving Image Recognition with Extremely Low FLOPs

1 code implementation ICCV 2021 Yunsheng Li, Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Lu Yuan, Zicheng Liu, Lei Zhang, Nuno Vasconcelos

This paper aims at addressing the problem of substantial performance degradation at extremely low computational cost (e. g. 5M FLOPs on ImageNet classification).

Improve Unsupervised Pretraining for Few-label Transfer

no code implementations ICCV 2021 Suichan Li, Dongdong Chen, Yinpeng Chen, Lu Yuan, Lei Zhang, Qi Chu, Bin Liu, Nenghai Yu

Unsupervised pretraining has achieved great success and many recent works have shown unsupervised pretraining can achieve comparable or even slightly better transfer performance than supervised pretraining on downstream target datasets.

Clustering Contrastive Learning

Dynamic Transfer for Multi-Source Domain Adaptation

1 code implementation CVPR 2021 Yunsheng Li, Lu Yuan, Yinpeng Chen, Pei Wang, Nuno Vasconcelos

However, such a static model is difficult to handle conflicts across multiple domains, and suffers from a performance degradation in both source domains and target domain.

Domain Adaptation

Revisiting Dynamic Convolution via Matrix Decomposition

1 code implementation ICLR 2021 Yunsheng Li, Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Ye Yu, Lu Yuan, Zicheng Liu, Mei Chen, Nuno Vasconcelos

It has two limitations: (a) it increases the number of convolutional weights by K-times, and (b) the joint optimization of dynamic attention and static convolution kernels is challenging.

Dimensionality Reduction

Stronger NAS with Weaker Predictors

1 code implementation NeurIPS 2021 Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang Wang, Zicheng Liu, Mei Chen, Lu Yuan

We propose a paradigm shift from fitting the whole architecture space using one strong predictor, to progressively fitting a search path towards the high-performance sub-space through a set of weaker predictors.

Neural Architecture Search

Weak NAS Predictor Is All You Need

no code implementations1 Jan 2021 Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang Wang, Zicheng Liu, Mei Chen, Lu Yuan

Rather than expecting a single strong predictor to model the whole space, we seek a progressive line of weak predictors that can connect a path to the best architecture, thus greatly simplifying the learning task of each predictor.

Neural Architecture Search

Dynamic DETR: End-to-End Object Detection With Dynamic Attention

no code implementations ICCV 2021 Xiyang Dai, Yinpeng Chen, Jianwei Yang, Pengchuan Zhang, Lu Yuan, Lei Zhang

To mitigate the second limitation of learning difficulty, we introduce a dynamic decoder by replacing the cross-attention module with a ROI-based dynamic attention in the Transformer decoder.

Decoder object-detection +1

Are Fewer Labels Possible for Few-shot Learning?

no code implementations10 Dec 2020 Suichan Li, Dongdong Chen, Yinpeng Chen, Lu Yuan, Lei Zhang, Qi Chu, Nenghai Yu

We conduct experiments on 10 different few-shot target datasets, and our average few-shot performance outperforms both vanilla inductive unsupervised transfer and supervised transfer by a large margin.

Clustering Few-Shot Learning

MicroNet: Towards Image Recognition with Extremely Low FLOPs

no code implementations24 Nov 2020 Yunsheng Li, Yinpeng Chen, Xiyang Dai, Dongdong Chen, Mengchen Liu, Lu Yuan, Zicheng Liu, Lei Zhang, Nuno Vasconcelos

In this paper, we present MicroNet, which is an efficient convolutional neural network using extremely low computational cost (e. g. 6 MFLOPs on ImageNet classification).

DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search

no code implementations ECCV 2020 Xiyang Dai, Dong-Dong Chen, Mengchen Liu, Yinpeng Chen, Lu Yuan

One common way is searching on a smaller proxy dataset (e. g., CIFAR-10) and then transferring to the target task (e. g., ImageNet).

Neural Architecture Search

Dynamic ReLU

2 code implementations ECCV 2020 Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dong-Dong Chen, Lu Yuan, Zicheng Liu

Rectified linear units (ReLU) are commonly used in deep neural networks.

Dynamic Convolution: Attention over Convolution Kernels

5 code implementations CVPR 2020 Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dong-Dong Chen, Lu Yuan, Zicheng Liu

Light-weight convolutional neural networks (CNNs) suffer performance degradation as their low computational budgets constrain both the depth (number of convolution layers) and the width (number of channels) of CNNs, resulting in limited representation capability.

Image Classification Keypoint Detection

Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning

1 code implementation17 Nov 2019 Fuxun Yu, Di Wang, Yinpeng Chen, Nikolaos Karianakis, Tong Shen, Pei Yu, Dimitrios Lymberopoulos, Sidi Lu, Weisong Shi, Xiang Chen

In this work, we show that such adversarial-based methods can only reduce the domain style gap, but cannot address the domain content distribution gap that is shown to be important for object detectors.

Object object-detection +2

Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation

3 code implementations11 Jul 2019 Kevin Lin, Lijuan Wang, Kun Luo, Yinpeng Chen, Zicheng Liu, Ming-Ting Sun

On the other hand, if part labels are also available in the real-images during training, our method outperforms the supervised state-of-the-art methods by a large margin.

 Ranked #1 on Human Part Segmentation on PASCAL-Part (using extra training data)

Domain Adaptation Human Part Segmentation +3

Large Scale Incremental Learning

4 code implementations CVPR 2019 Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, Yun Fu

We believe this is because of the combination of two factors: (a) the data imbalance between the old and new classes, and (b) the increasing number of visually similar classes.

Class Incremental Learning Incremental Learning

Rethinking Classification and Localization for Object Detection

2 code implementations CVPR 2020 Yue Wu, Yinpeng Chen, Lu Yuan, Zicheng Liu, Lijuan Wang, Hongzhi Li, Yun Fu

Two head structures (i. e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks.

Classification General Classification +3

Incremental Classifier Learning with Generative Adversarial Networks

no code implementations2 Feb 2018 Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, Zhengyou Zhang, Yun Fu

To address these problems, we propose (a) a new loss function to combine the cross-entropy loss and distillation loss, (b) a simple way to estimate and remove the unbalance between the old and new classes , and (c) using Generative Adversarial Networks (GANs) to generate historical data and select representative exemplars during generation.

General Classification

Tensor-Based Human Body Modeling

no code implementations CVPR 2013 Yinpeng Chen, Zicheng Liu, Zhengyou Zhang

In this paper, we present a novel approach to model 3D human body with variations on both human shape and pose, by exploring a tensor decomposition technique.

3D Reconstruction Tensor Decomposition

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