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Sheng Tang

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8 papers

1 author row

AAAI Conference 2026 Conference Paper

Beyond Semantic Features: Pixel-level Mapping for Generalized AI-Generated Image Detection

Chenming Zhou
Jiaan Wang
Yu Li
Lei Li
Juan Cao
Sheng Tang

The rapid evolution of generative technologies necessitates reliable methods for detecting AI-generated images. A critical limitation of current detectors is their failure to generalize to images from unseen generative models, as they often overfit to source-specific semantic cues rather than learning universal generative artifacts. To overcome this, we introduce a simple yet remarkably effective pixel-level mapping pre-processing step to disrupt the pixel value distribution of images and break the fragile, non-essential semantic patterns that detectors commonly exploit as shortcuts. This forces the detector to focus on more fundamental and generalizable high-frequency traces inherent to the image generation process. Through comprehensive experiments on GAN and diffusion-based generators, we show that our approach significantly boosts the cross-generator performance of state-of-the-art detectors. Extensive analysis further verifies our hypothesis that the disruption of semantic cues is the key to generalization.

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NeurIPS Conference 2024 Conference Paper

Self-Supervised Adversarial Training via Diverse Augmented Queries and Self-Supervised Double Perturbation

Ruize Zhang
Sheng Tang
Juan Cao

Recently, there have been some works studying self-supervised adversarial training, a learning paradigm that learns robust features without labels. While those works have narrowed the performance gap between self-supervised adversarial training (SAT) and supervised adversarial training (supervised AT), a well-established formulation of SAT and its connections with supervised AT are under-explored. Based on a simple SAT benchmark, we find that SAT still faces the problem of large robust generalization gap and degradation on natural samples. We hypothesize this is due to the lack of data complexity and model regularization and propose a method named as DAQ-SDP (Diverse Augmented Queries Self-supervised Double Perturbation). We first challenge the previous conclusion that complex data augmentations degrade robustness in SAT by using diversely augmented samples as queries to guide adversarial training. Inspired by previous works in supervised AT, we then incorporate a self-supervised double perturbation scheme to self-supervised learning (SSL), which promotes robustness transferable to downstream classification. Our work can be seamlessly combined with models pretrained by different SSL frameworks without revising the learning objectives and helps to bridge the gap between SAT and AT. Our method also improves both robust and natural accuracies across different SSL frameworks. Our code is available at https: //github. com/rzzhang222/DAQ-SDP.

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IJCAI Conference 2019 Conference Paper

Boundary Perception Guidance: A Scribble-Supervised Semantic Segmentation Approach

Bin Wang
Guojun Qi
Sheng Tang
Tianzhu Zhang
Yunchao Wei
Linghui Li
Yongdong Zhang

Semantic segmentation suffers from the fact that densely annotated masks are expensive to obtain. To tackle this problem, we aim at learning to segment by only leveraging scribbles that are much easier to collect for supervision. To fully explore the limited pixel-level annotations from scribbles, we present a novel Boundary Perception Guidance (BPG) approach, which consists of two basic components, i. e. , prediction refinement and boundary regression. Specifically, the prediction refinement progressively makes a better segmentation by adopting an iterative upsampling and a semantic feature enhancement strategy. In the boundary regression, we employ class-agnostic edge maps for supervision to effectively guide the segmentation network in localizing the boundaries between different semantic regions, leading to producing finer-grained representation of feature maps for semantic segmentation. The experiment results on the PASCAL VOC 2012 demonstrate the proposed BPG achieves mIoU of 73. 2% without fully connected Conditional Random Field (CRF) and 76. 0% with CRF, setting up the new state-of-the-art in literature.

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AAAI Conference 2018 Conference Paper

Auto-Balanced Filter Pruning for Efficient Convolutional Neural Networks

Xiaohan Ding
Guiguang Ding
Jungong Han
Sheng Tang

In recent years considerable research efforts have been devoted to compression techniques of convolutional neural networks (CNNs). Many works so far have focused on CNN connection pruning methods which produce sparse parameter tensors in convolutional or fully-connected layers. It has been demonstrated in several studies that even simple methods can effectively eliminate connections of a CNN. However, since these methods make parameter tensors just sparser but no smaller, the compression may not transfer directly to acceleration without support from specially designed hardware. In this paper, we propose an iterative approach named Auto-balanced Filter Pruning, where we pre-train the network in an innovative auto-balanced way to transfer the representational capacity of its convolutional layers to a fraction of the ﬁlters, prune the redundant ones, then re-train it to restore the accuracy. In this way, a smaller version of the original network is learned and the ﬂoating-point operations (FLOPs) are reduced. By applying this method on several common CNNs, we show that a large portion of the ﬁlters can be discarded without obvious accuracy drop, leading to signiﬁcant reduction of computational burdens. Concretely, we reduce the inference cost of LeNet-5 on MNIST, VGG-16 and ResNet-56 on CIFAR-10 by 95. 1%, 79. 7% and 60. 9%, respectively.

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IJCAI Conference 2018 Conference Paper

High Resolution Feature Recovering for Accelerating Urban Scene Parsing

Rui Zhang
Sheng Tang
Luoqi Liu
Yongdong Zhang
Jintao Li
Shuicheng Yan

Both accuracy and speed are equally important in urban scene parsing. Most of the existing methods mainly focus on improving parsing accuracy, ignoring the problem of low inference speed due to large-sized input and high resolution feature maps. To tackle this issue, we propose a High Resolution Feature Recovering (HRFR) framework to accelerate a given parsing network. A Super-Resolution Recovering module is employed to recover features of large original-sized images from features of down-sampled input. Therefore, our framework can combine the advantages of (1) fast speed of networks with down-sampled input and (2) high accuracy of networks with large original-sized input. Additionally, we employ auxiliary intermediate supervision and boundary region re-weighting to facilitate the optimization of the network. Extensive experiments on the two challenging Cityscapes and CamVid datasets well demonstrate the effectiveness of the proposed HRFR framework, which can accelerate the scene parsing inference process by about 3. 0x speedup from 1/2 down-sampled input with negligible accuracy reduction.

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AAAI Conference 2018 Conference Paper

Zero-Shot Learning With Attribute Selection

Yuchen Guo
Guiguang Ding
Jungong Han
Sheng Tang

Zero-shot learning (ZSL) is regarded as an effective way to construct classiﬁcation models for target classes which have no labeled samples available. The basic framework is to transfer knowledge from (different) auxiliary source classes having sufﬁcient labeled samples with some attributes shared by target and source classes as bridge. Attributes play an important role in ZSL but they have not gained sufﬁcient attention in recent years. Previous works mostly assume attributes are perfect and treat each attribute equally. However, as shown in this paper, different attributes have different properties, such as their class distribution, variance, and entropy, which may have considerable impact on ZSL accuracy if treated equally. Based on this observation, in this paper we propose to use a subset of attributes, instead of the whole set, for building ZSL models. The attribute selection is conducted by considering the information amount and predictability under a novel joint optimization framework. To our knowledge, this is the ﬁrst work that notices the inﬂuence of attributes themselves and proposes to use a reﬁned attribute set for ZSL. Since our approach focuses on selecting good attributes for ZSL, it can be combined to any attribute based ZSL approaches so as to augment their performance. Experiments on four ZSL benchmarks demonstrate that our approach can improve zeroshot classiﬁcation accuracy and yield state-of-the-art results.

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IJCAI Conference 2017 Conference Paper

Global-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions

Rui Zhang
Sheng Tang
Min Lin
Jintao Li
Shuicheng Yan

Most of existing scene parsing methods suffer from the serious problems of both inconsistent parsing results and object boundary shift. To tackle these problems, we first propose an iterative Global-residual Refinement Network (GRN) through exploiting global contextual information to predict the parsing residuals and iteratively smoothen the inconsistent parsing labels. Furthermore, we propose a Local-boundary Refinement Network (LRN) to learn the position-adaptive propagation coefficients so that local contextual information from neighbors can be optimally captured for refining object boundaries. Finally, we cascade the proposed two refinement networks after a fully residual convolutional neural network within a uniform framework. Extensive experiments on ADE20K and Cityscapes datasets well demonstrate the effectiveness of the two refinement methods for refining scene parsing predictions.

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AAAI Conference 2017 Conference Paper

Image Caption with Global-Local Attention

Linghui Li
Sheng Tang
Lixi Deng
Yongdong Zhang
Qi Tian

Image caption is becoming important in the ﬁeld of artiﬁcial intelligence. Most existing methods based on CNN-RNN framework suffer from the problems of object missing and misprediction due to the mere use of global representation at image-level. To address these problems, in this paper, we propose a global-local attention (GLA) method by integrating local representation at object-level with global representation at image-level through attention mechanism. Thus, our proposed method can pay more attention to how to predict the salient objects more precisely with high recall while keeping context information at image-level cocurrently. Therefore, our proposed GLA method can generate more relevant sentences, and achieve the state-of-the-art performance on the well-known Microsoft COCO caption dataset with several popular metrics.

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