Search Results for author: Shan You

Found 47 papers, 24 papers with code

LocalMamba: Visual State Space Model with Windowed Selective Scan

1 code implementation14 Mar 2024 Tao Huang, Xiaohuan Pei, Shan You, Fei Wang, Chen Qian, Chang Xu

This paper posits that the key to enhancing Vision Mamba (ViM) lies in optimizing scan directions for sequence modeling.

Active Generation for Image Classification

no code implementations11 Mar 2024 Tao Huang, Jiaqi Liu, Shan You, Chang Xu

Recently, the growing capabilities of deep generative models have underscored their potential in enhancing image classification accuracy.

Active Learning Classification +3

Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models

1 code implementation NeurIPS 2023 Yichao Cao, Qingfei Tang, Xiu Su, Chen Song, Shan You, Xiaobo Lu, Chang Xu

We conduct a deep analysis of the three hierarchical features inherent in visual HOI detectors and propose a method for high-level relation extraction aimed at VL foundation models, which we call HO prompt-based learning.

Human-Object Interaction Detection Relation Extraction +1

CoNe: Contrast Your Neighbours for Supervised Image Classification

1 code implementation21 Aug 2023 Mingkai Zheng, Shan You, Lang Huang, Xiu Su, Fei Wang, Chen Qian, Xiaogang Wang, Chang Xu

Moreover, to further boost the performance, we propose ``distributional consistency" as a more informative regularization to enable similar instances to have a similar probability distribution.

Classification Image Classification

Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection

no code implementations ICCV 2023 Yichao Cao, Qingfei Tang, Feng Yang, Xiu Su, Shan You, Xiaobo Lu, Chang Xu

Human-Object Interaction (HOI) detection is a challenging computer vision task that requires visual models to address the complex interactive relationship between humans and objects and predict HOI triplets.

Human-Object Interaction Detection Sentence +1

Knowledge Diffusion for Distillation

1 code implementation NeurIPS 2023 Tao Huang, Yuan Zhang, Mingkai Zheng, Shan You, Fei Wang, Chen Qian, Chang Xu

To address this, we propose to denoise student features using a diffusion model trained by teacher features.

Denoising Image Classification +4

Can GPT-4 Perform Neural Architecture Search?

1 code implementation21 Apr 2023 Mingkai Zheng, Xiu Su, Shan You, Fei Wang, Chen Qian, Chang Xu, Samuel Albanie

We investigate the potential of GPT-4~\cite{gpt4} to perform Neural Architecture Search (NAS) -- the task of designing effective neural architectures.

Navigate Neural Architecture Search

Boosting Semi-Supervised Semantic Segmentation with Probabilistic Representations

1 code implementation26 Oct 2022 Haoyu Xie, Changqi Wang, Mingkai Zheng, Minjing Dong, Shan You, Chong Fu, Chang Xu

In prevalent pixel-wise contrastive learning solutions, the model maps pixels to deterministic representations and regularizes them in the latent space.

Contrastive Learning Semi-Supervised Semantic Segmentation

ScaleNet: Searching for the Model to Scale

1 code implementation15 Jul 2022 Jiyang Xie, Xiu Su, Shan You, Zhanyu Ma, Fei Wang, Chen Qian

Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales.

LightViT: Towards Light-Weight Convolution-Free Vision Transformers

1 code implementation12 Jul 2022 Tao Huang, Lang Huang, Shan You, Fei Wang, Chen Qian, Chang Xu

Vision transformers (ViTs) are usually considered to be less light-weight than convolutional neural networks (CNNs) due to the lack of inductive bias.

Image Classification Inductive Bias +3

Masked Distillation with Receptive Tokens

1 code implementation29 May 2022 Tao Huang, Yuan Zhang, Shan You, Fei Wang, Chen Qian, Jian Cao, Chang Xu

To obtain a group of masks, the receptive tokens are learned via the regular task loss but with teacher fixed, and we also leverage a Dice loss to enrich the diversity of learned masks.

object-detection Object Detection +1

Green Hierarchical Vision Transformer for Masked Image Modeling

1 code implementation26 May 2022 Lang Huang, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, Toshihiko Yamasaki

We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision Transformers (ViTs), allowing the hierarchical ViTs to discard masked patches and operate only on the visible ones.

Object Detection

Knowledge Distillation from A Stronger Teacher

2 code implementations21 May 2022 Tao Huang, Shan You, Fei Wang, Chen Qian, Chang Xu

In this paper, we show that simply preserving the relations between the predictions of teacher and student would suffice, and propose a correlation-based loss to capture the intrinsic inter-class relations from the teacher explicitly.

Ranked #2 on Knowledge Distillation on ImageNet (using extra training data)

Image Classification Knowledge Distillation +2

Learning Where to Learn in Cross-View Self-Supervised Learning

1 code implementation CVPR 2022 Lang Huang, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, Toshihiko Yamasaki

In this paper, we present a new approach, Learning Where to Learn (LEWEL), to adaptively aggregate spatial information of features, so that the projected embeddings could be exactly aligned and thus guide the feature learning better.

object-detection Object Detection +3

Searching for Network Width with Bilaterally Coupled Network

1 code implementation25 Mar 2022 Xiu Su, Shan You, Jiyang Xie, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu

In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately.

Fairness

DyRep: Bootstrapping Training with Dynamic Re-parameterization

2 code implementations CVPR 2022 Tao Huang, Shan You, Bohan Zhang, Yuxuan Du, Fei Wang, Chen Qian, Chang Xu

Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks.

Relational Self-Supervised Learning

no code implementations16 Mar 2022 Mingkai Zheng, Shan You, Fei Wang, Chen Qian, ChangShui Zhang, Xiaogang Wang, Chang Xu

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations.

Contrastive Learning Relation +2

Relational Surrogate Loss Learning

1 code implementation ICLR 2022 Tao Huang, Zekang Li, Hua Lu, Yong Shan, Shusheng Yang, Yang Feng, Fei Wang, Shan You, Chang Xu

Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e. g., average precision and F1 score.

Image Classification Machine Reading Comprehension +3

GreedyNASv2: Greedier Search with a Greedy Path Filter

no code implementations CVPR 2022 Tao Huang, Shan You, Fei Wang, Chen Qian, ChangShui Zhang, Xiaogang Wang, Chang Xu

In this paper, we leverage an explicit path filter to capture the characteristics of paths and directly filter those weak ones, so that the search can be thus implemented on the shrunk space more greedily and efficiently.

ReSSL: Relational Self-Supervised Learning with Weak Augmentation

2 code implementations NeurIPS 2021 Mingkai Zheng, Shan You, Fei Wang, Chen Qian, ChangShui Zhang, Xiaogang Wang, Chang Xu

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations.

Contrastive Learning Relation +2

ViTAS: Vision Transformer Architecture Search

1 code implementation25 Jun 2021 Xiu Su, Shan You, Jiyang Xie, Mingkai Zheng, Fei Wang, Chen Qian, ChangShui Zhang, Xiaogang Wang, Chang Xu

Vision transformers (ViTs) inherited the success of NLP but their structures have not been sufficiently investigated and optimized for visual tasks.

Inductive Bias Neural Architecture Search

K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets

no code implementations11 Jun 2021 Xiu Su, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu

The operation weight for each path is represented as a convex combination of items in a dictionary with a simplex code.

BCNet: Searching for Network Width with Bilaterally Coupled Network

no code implementations CVPR 2021 Xiu Su, Shan You, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu

In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately.

Prioritized Architecture Sampling with Monto-Carlo Tree Search

1 code implementation CVPR 2021 Xiu Su, Tao Huang, Yanxi Li, Shan You, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu

One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once.

Neural Architecture Search

Locally Free Weight Sharing for Network Width Search

no code implementations ICLR 2021 Xiu Su, Shan You, Tao Huang, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu

In this paper, to better evaluate each width, we propose a locally free weight sharing strategy (CafeNet) accordingly.

Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search

no code implementations CVPR 2021 Yibo Yang, Shan You, Hongyang Li, Fei Wang, Chen Qian, Zhouchen Lin

Our method enables differentiable sparsification, and keeps the derived architecture equivalent to that of Engine-cell, which further improves the consistency between search and evaluation.

Neural Architecture Search

Explicit Learning Topology for Differentiable Neural Architecture Search

no code implementations1 Jan 2021 Tao Huang, Shan You, Yibo Yang, Zhuozhuo Tu, Fei Wang, Chen Qian, ChangShui Zhang

Differentiable neural architecture search (NAS) has gained much success in discovering more flexible and diverse cell types.

Neural Architecture Search

Wasserstein Distributionally Robust Optimization: A Three-Player Game Framework

no code implementations1 Jan 2021 Zhuozhuo Tu, Shan You, Tao Huang, DaCheng Tao

Wasserstein distributionally robust optimization (DRO) has recently received significant attention in machine learning due to its connection to generalization, robustness and regularization.

EnTranNAS: Towards Closing the Gap between the Architectures in Search and Evaluation

no code implementations1 Jan 2021 Yibo Yang, Shan You, Hongyang Li, Fei Wang, Chen Qian, Zhouchen Lin

The Engine-cell is differentiable for architecture search, while the Transit-cell only transits the current sub-graph by architecture derivation.

Neural Architecture Search

Learning With Privileged Tasks

no code implementations ICCV 2021 Yuru Song, Zan Lou, Shan You, Erkun Yang, Fei Wang, Chen Qian, ChangShui Zhang, Xiaogang Wang

Concretely, we introduce a privileged parameter so that the optimization direction does not necessarily follow the gradient from the privileged tasks, but concentrates more on the target tasks.

Multi-Task Learning

Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space

1 code implementation NeurIPS 2020 Shangchen Du, Shan You, Xiaojie Li, Jianlong Wu, Fei Wang, Chen Qian, ChangShui Zhang

In this paper, we examine the diversity of teacher models in the gradient space and regard the ensemble knowledge distillation as a multi-objective optimization problem so that we can determine a better optimization direction for the training of student network.

Knowledge Distillation

Stretchable Cells Help DARTS Search Better

no code implementations18 Nov 2020 Tao Huang, Shan You, Yibo Yang, Zhuozhuo Tu, Fei Wang, Chen Qian, ChangShui Zhang

However, even for this consistent search, the searched cells often suffer from poor performance, especially for the supernet with fewer layers, as current DARTS methods are prone to wide and shallow cells, and this topology collapse induces sub-optimal searched cells.

Neural Architecture Search

Data Agnostic Filter Gating for Efficient Deep Networks

no code implementations28 Oct 2020 Xiu Su, Shan You, Tao Huang, Hongyan Xu, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu

To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e. g., FLOPs).

Quantum circuit architecture search for variational quantum algorithms

1 code implementation20 Oct 2020 Yuxuan Du, Tao Huang, Shan You, Min-Hsiu Hsieh, DaCheng Tao

Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices.

Quantum Differentially Private Sparse Regression Learning

no code implementations23 Jul 2020 Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, DaCheng Tao

The eligibility of various advanced quantum algorithms will be questioned if they can not guarantee privacy.

BIG-bench Machine Learning regression

Bringing Giant Neural Networks Down to Earth with Unlabeled Data

no code implementations13 Jul 2019 Yehui Tang, Shan You, Chang Xu, Boxin Shi, Chao Xu

Specifically, we exploit the unlabeled data to mimic the classification characteristics of giant networks, so that the original capacity can be preserved nicely.

Privileged Multi-label Learning

no code implementations25 Jan 2017 Shan You, Chang Xu, Yunhe Wang, Chao Xu, DaCheng Tao

This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems.

Multi-Label Learning

Streaming Label Learning for Modeling Labels on the Fly

no code implementations19 Apr 2016 Shan You, Chang Xu, Yunhe Wang, Chao Xu, DaCheng Tao

The core of SLL is to explore and exploit the relationships between new labels and past labels and then inherit the relationship into hypotheses of labels to boost the performance of new classifiers.

Multi-Label Learning

Parts for the Whole: The DCT Norm for Extreme Visual Recovery

no code implementations19 Apr 2016 Yunhe Wang, Chang Xu, Shan You, DaCheng Tao, Chao Xu

Here we study the extreme visual recovery problem, in which over 90\% of pixel values in a given image are missing.

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