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Enhua Wu

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12 papers

2 author rows

ICLR Conference 2025 Conference Paper

Denoising with a Joint-Embedding Predictive Architecture

Dengsheng Chen
Jie Hu 0019
Xiaoming Wei
Enhua Wu

Joint-embedding predictive architectures (JEPAs) have shown substantial promise in self-supervised representation learning, yet their application in generative modeling remains underexplored. Conversely, diffusion models have demonstrated significant efficacy in modeling arbitrary probability distributions. In this paper, we introduce Denoising with a Joint-Embedding Predictive Architecture (D-JEPA), pioneering the integration of JEPA within generative modeling. By recognizing JEPA as a form of masked image modeling, we reinterpret it as a generalized next-token prediction strategy, facilitating data generation in an auto-regressive manner. Furthermore, we incorporate diffusion loss to model the per-token probability distribution, enabling data generation in a continuous space. We also adapt flow matching loss as an alternative to diffusion loss, thereby enhancing the flexibility of D-JEPA. Empirically, with increased GFLOPs, D-JEPA consistently achieves lower FID scores with fewer training epochs, indicating its good scalability. Our base, large, and huge models outperform all previous generative models across all scales on ImageNet conditional generation benchmarks. Beyond image generation, D-JEPA is well-suited for other continuous data modeling, including video and audio.

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IJCAI Conference 2024 Conference Paper

A Transformer-Based Adaptive Prototype Matching Network for Few-Shot Semantic Segmentation

Sihan Chen
Yadang Chen
Yuhui Zheng
Zhi-Xin Yang
Enhua Wu

Few-shot semantic segmentation (FSS) aims to generate a model for segmenting novel classes using a limited number of annotated samples. Previous FSS methods have shown sensitivity to background noise due to inherent bias, attention bias, and spatial-aware bias. In this study, we propose a Transformer-Based Adaptive Prototype Matching Network to establish robust matching relationships by improving the semantic and spatial perception of query features. The model includes three modules: target enhancement module (TEM), dual constraint aggregation module (DCAM), and dual classification module (DCM). In particular, TEM mitigates inherent bias by exploring the relevance of multi-scale local context to enhance foreground features. Then, DCAM addresses attention bias through the dual semantic-aware attention mechanism to strengthen constraints. Finally, the DCM module decouples the segmentation task into semantic alignment and spatial alignment to alleviate spatial-aware bias. Extensive experiments on PASCAL-5i and COCO-20i confirm the effectiveness of our approach.

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AAAI Conference 2024 Conference Paper

Real3D: The Curious Case of Neural Scene Degeneration

Dengsheng Chen
Jie Hu
Xiaoming Wei
Enhua Wu

Despite significant progress in utilizing pre-trained text-to-image diffusion models to guide the creation of 3D scenes, these methods often struggle to generate scenes that are sufficiently realistic, leading to "neural scene degeneration". In this work, we propose a new 3D scene generation model called Real3D. Specifically, Real3D designs a pipeline from a NeRF-like implicit renderer to a tetrahedrons-based explicit renderer, greatly improving the neural network's ability to generate various neural scenes. Moreover, Real3D introduces an additional discriminator to prevent neural scenes from falling into undesirable local optima, thus avoiding the degeneration phenomenon. Our experimental results demonstrate that Real3D outperforms all existing state-of-the-art text-to-3D generation methods, providing valuable insights to facilitate the development of learning-based 3D scene generation approaches.

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AAAI Conference 2023 Conference Paper

3D Human Pose Lifting with Grid Convolution

Yangyuxuan Kang
Yuyang Liu
Anbang Yao
Shandong Wang
Enhua Wu

Existing lifting networks for regressing 3D human poses from 2D single-view poses are typically constructed with linear layers based on graph-structured representation learning. In sharp contrast to them, this paper presents Grid Convolution (GridConv), mimicking the wisdom of regular convolution operations in image space. GridConv is based on a novel Semantic Grid Transformation (SGT) which leverages a binary assignment matrix to map the irregular graph-structured human pose onto a regular weave-like grid pose representation joint by joint, enabling layer-wise feature learning with GridConv operations. We provide two ways to implement SGT, including handcrafted and learnable designs. Surprisingly, both designs turn out to achieve promising results and the learnable one is better, demonstrating the great potential of this new lifting representation learning formulation. To improve the ability of GridConv to encode contextual cues, we introduce an attention module over the convolutional kernel, making grid convolution operations input-dependent, spatial-aware and grid-specific. We show that our fully convolutional grid lifting network outperforms state-of-the-art methods with noticeable margins under (1) conventional evaluation on Human3.6M and (2) cross-evaluation on MPI-INF-3DHP. Code is available at https://github.com/OSVAI/GridConv.

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ICLR Conference 2023 Conference Paper

Rethinking skip connection model as a learnable Markov chain

Dengsheng Chen
Jie Hu 0019
Wenwen Qiang
Xiaoming Wei
Enhua Wu

Over the past few years afterward the birth of ResNet, skip connection has become the defacto standard for the design of modern architectures due to its widespread adoption, easy optimization, and proven performance. Prior work has explained the effectiveness of the skip connection mechanism from different perspectives. In this work, we deep dive into the model's behaviors with skip connections which can be formulated as a learnable Markov chain. An efficient Markov chain is preferred as it always maps the input data to the target domain in a better way. However, while a model is explained as a Markov chain, it is not guaranteed to be optimized following an efficient Markov chain by existing SGD-based optimizers prone to getting trapped in local optimal points. In order to move towards a more efficient Markov chain, we propose a simple routine of penal connection to make any residual-like model become a learnable Markov chain. Aside from that, the penal connection can also be viewed as a particular model regularization and can be easily implemented with one line of code in the most popular deep learning frameworks. The encouraging experimental results in multi-modal translation and image recognition empirically confirm our conjecture of the learnable Markov chain view and demonstrate the superiority of the proposed penal connection.

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AAAI Conference 2022 Conference Paper

Elastic-Link for Binarized Neural Networks

Jie Hu
Ziheng Wu
Vince Tan
Zhilin Lu
Mengze Zeng
Enhua Wu

Recent work has shown that Binarized Neural Networks (BNNs) are able to greatly reduce computational costs and memory footprints, facilitating model deployment on resource-constrained devices. However, in comparison to their full-precision counterparts, BNNs suffer from severe accuracy degradation. Research aiming to reduce this accuracy gap has thus far largely focused on specific network architectures with few or no 1 × 1 convolutional layers, for which standard binarization methods do not work well. Because 1×1 convolutions are common in the design of modern architectures (e. g. GoogleNet, ResNet, DenseNet), it is crucial to develop a method to binarize them effectively for BNNs to be more widely adopted. In this work, we propose an “Elastic- Link” (EL) module to enrich information flow within a BNN by adaptively adding real-valued input features to the subsequent convolutional output features. The proposed EL module is easily implemented and can be used in conjunction with other methods for BNNs. We demonstrate that adding EL to BNNs produces a significant improvement on the challenging large-scale ImageNet dataset. For example, we raise the top-1 accuracy of binarized ResNet26 from 57. 9% to 64. 0%. EL also aids convergence in the training of binarized MobileNet, for which a top-1 accuracy of 56. 4% is achieved. Finally, with the integration of ReActNet, it yields a new state-of-the-art result of 71. 9% top-1 accuracy.

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NeurIPS Conference 2022 Conference Paper

Redistribution of Weights and Activations for AdderNet Quantization

Ying Nie
Kai Han
Haikang Diao
Chuanjian Liu
Enhua Wu
Yunhe Wang

Adder Neural Network (AdderNet) provides a new way for developing energy-efficient neural networks by replacing the expensive multiplications in convolution with cheaper additions (i. e. , L1-norm). To achieve higher hardware efficiency, it is necessary to further study the low-bit quantization of AdderNet. Due to the limitation that the commutative law in multiplication does not hold in L1-norm, the well-established quantization methods on convolutional networks cannot be applied on AdderNets. Thus, the existing AdderNet quantization techniques propose to use only one shared scale to quantize both the weights and activations simultaneously. Admittedly, such an approach can keep the commutative law in the L1-norm quantization process, while the accuracy drop after low-bit quantization cannot be ignored. To this end, we first thoroughly analyze the difference on distributions of weights and activations in AdderNet and then propose a new quantization algorithm by redistributing the weights and the activations. Specifically, the pre-trained full-precision weights in different kernels are clustered into different groups, then the intra-group sharing and inter-group independent scales can be adopted. To further compensate the accuracy drop caused by the distribution difference, we then develop a lossless range clamp scheme for weights and a simple yet effective outliers clamp strategy for activations. Thus, the functionality of full-precision weights and the representation ability of full-precision activations can be fully preserved. The effectiveness of the proposed quantization method for AdderNet is well verified on several benchmarks, e. g. , our 4-bit post-training quantized adder ResNet-18 achieves an 66. 5% top-1 accuracy on the ImageNet with comparable energy efficiency, which is about 8. 5% higher than that of the previous AdderNet quantization methods. Code will be available at https: //gitee. com/mindspore/models/tree/master/research/cv/AdderQuant.

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NeurIPS Conference 2022 Conference Paper

Vision GNN: An Image is Worth Graph of Nodes

Kai Han
Yunhe Wang
Jianyuan Guo
Yehui Tang
Enhua Wu

Network architecture plays a key role in the deep learning-based computer vision system. The widely-used convolutional neural network and transformer treat the image as a grid or sequence structure, which is not flexible to capture irregular and complex objects. In this paper, we propose to represent the image as a graph structure and introduce a new \emph{Vision GNN} (ViG) architecture to extract graph-level feature for visual tasks. We first split the image to a number of patches which are viewed as nodes, and construct a graph by connecting the nearest neighbors. Based on the graph representation of images, we build our ViG model to transform and exchange information among all the nodes. ViG consists of two basic modules: Grapher module with graph convolution for aggregating and updating graph information, and FFN module with two linear layers for node feature transformation. Both isotropic and pyramid architectures of ViG are built with different model sizes. Extensive experiments on image recognition and object detection tasks demonstrate the superiority of our ViG architecture. We hope this pioneering study of GNN on general visual tasks will provide useful inspiration and experience for future research. The PyTorch code is available at \url{https: //github. com/huawei-noah/Efficient-AI-Backbones} and the MindSpore code is available at \url{https: //gitee. com/mindspore/models}.

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NeurIPS Conference 2021 Conference Paper

Dynamic Resolution Network

Mingjian Zhu
Kai Han
Enhua Wu
Qiulin Zhang
Ying Nie
Zhenzhong Lan
Yunhe Wang

Deep convolutional neural networks (CNNs) are often of sophisticated design with numerous learnable parameters for the accuracy reason. To alleviate the expensive costs of deploying them on mobile devices, recent works have made huge efforts for excavating redundancy in pre-defined architectures. Nevertheless, the redundancy on the input resolution of modern CNNs has not been fully investigated, i. e. , the resolution of input image is fixed. In this paper, we observe that the smallest resolution for accurately predicting the given image is different using the same neural network. To this end, we propose a novel dynamic-resolution network (DRNet) in which the input resolution is determined dynamically based on each input sample. Wherein, a resolution predictor with negligible computational costs is explored and optimized jointly with the desired network. Specifically, the predictor learns the smallest resolution that can retain and even exceed the original recognition accuracy for each image. During the inference, each input image will be resized to its predicted resolution for minimizing the overall computation burden. We then conduct extensive experiments on several benchmark networks and datasets. The results show that our DRNet can be embedded in any off-the-shelf network architecture to obtain a considerable reduction in computational complexity. For instance, DR-ResNet-50 achieves similar performance with an about 34% computation reduction, while gaining 1. 4% accuracy increase with 10% computation reduction compared to the original ResNet-50 on ImageNet. Code will be available at https: //gitee. com/mindspore/models/tree/master/research/cv/DRNet.

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NeurIPS Conference 2021 Conference Paper

Transformer in Transformer

Kai Han
An Xiao
Enhua Wu
Jianyuan Guo
Chunjing Xu
Yunhe Wang

Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural images are of high complexity with abundant detail and color information, the granularity of the patch dividing is not fine enough for excavating features of objects in different scales and locations. In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT). Specifically, we regard the local patches (\eg, 16$\times$16) as “visual sentences” and present to further divide them into smaller patches (\eg, 4$\times$4) as “visual words”. The attention of each word will be calculated with other words in the given visual sentence with negligible computational costs. Features of both words and sentences will be aggregated to enhance the representation ability. Experiments on several benchmarks demonstrate the effectiveness of the proposed TNT architecture, \eg, we achieve an 81. 5\% top-1 accuracy on the ImageNet, which is about 1. 7\% higher than that of the state-of-the-art visual transformer with similar computational cost. The PyTorch code is available at \url{https: //github. com/huawei-noah/CV-Backbones}, and the MindSpore code is available at \url{https: //gitee. com/mindspore/models/tree/master/research/cv/TNT}.

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ICML Conference 2020 Conference Paper

Training Binary Neural Networks through Learning with Noisy Supervision

Kai Han 0002
Yunhe Wang 0001
Yixing Xu
Chunjing Xu
Enhua Wu
Chang Xu 0002

This paper formalizes the binarization operations over neural networks from a learning perspective. In contrast to classical hand crafted rules (\eg hard thresholding) to binarize full-precision neurons, we propose to learn a mapping from full-precision neurons to the target binary ones. Each individual weight entry will not be binarized independently. Instead, they are taken as a whole to accomplish the binarization, just as they work together in generating convolution features. To help the training of the binarization mapping, the full-precision neurons after taking sign operations is regarded as some auxiliary supervision signal, which is noisy but still has valuable guidance. An unbiased estimator is therefore introduced to mitigate the influence of the supervision noise. Experimental results on benchmark datasets indicate that the proposed binarization technique attains consistent improvements over baselines.

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IJCAI Conference 2015 Conference Paper

Adaptive Sharing for Image Classification

Li Shen
Gang Sun
Zhouchen Lin
Qingming Huang
Enhua Wu

In this paper, we formulate the image classification problem in a multi-task learning framework. We propose a novel method to adaptively share information among tasks (classes). Different from imposing strong assumptions or discovering specific structures, the key insight in our method is to selectively extract and exploit the shared information among classes while capturing respective disparities simultaneously. It is achieved by estimating a composite of two sets of parameters with different regularization. Besides applying it for learning classifiers on pre-computed features, we also integrate the adaptive sharing with deep neural networks, whose discriminative power can be augmented by encoding class relationship. We further develop two strategies for solving the optimization problems in the two scenarios. Empirical results demonstrate that our method can significantly improve the classification performance by transferring knowledge appropriately.

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