Author name cluster

Zhenguo Yang

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

Multi-Task Test-time Adaptation via Gradient Consensus and Plasticity Constraint

Zhong Ye
Yu Hu
Zhenguo Yang

Multi-task test-time adaptation (MT-TTA) aims to adapt pre-trained models to dynamic environments during multi-task inference by leveraging unlabeled test data. This task is particularly challenging as different tasks respond divergently to distribution shifts, and mixed input streams containing both in-distribution (ID) and out-of-distribution (OOD) samples make the models after test-time adaptation prone to catastrophic forgetting of ID knowledge. Although the existing methods like M-TENT extend the classic test entropy minimization (TENT) by minimizing multi-task entropies and employing task-average gradient to adapt a model, it suffers from two key limitations: 1) the average gradient strategy proposed by M-TENT may exacerbate multi-task test-time optimization conflicts, harming individual tasks when gradients are directionally non-consensual; 2) aggressive updates on mixed ID/OOD data cause severe forgetting of ID knowledge. In this paper, we theoretically establish a formal connection between multi-task loss differences and test-time performance under the first-order Taylor analysis, demonstrating that consensual multi-task entropy reductions are likely to increase the performance, while non-consensual ones might decrease the performance. To this end, we propose Consensus-driven Constrained Multi-Task Test-Time Adaptation (CoCo-MT-TTA), consisting of 1) multi-task gradient consensus adaptation, which aligns cross-task gradient directions to seek a consensus gradient; 2) multi-task plasticity-constraint adaptation, which constrains parameter updates using second-moment statistics to preserve ID knowledge. Extensive experiments on benchmark datasets, including CelebA and PlantData, demonstrate that our method achieves an absolute improvement of up to 16.02% in mean ID/OOD F1-score (Mean I&O) under domain shifts over non-adapted models, outperforming the recent baselines.

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TIST Journal 2025 Journal Article

A Multi-Hop Graph Reasoning Network for Knowledge-Based VQA

Zihan Hu
Jiuxiang You
Zhenguo Yang
Xiaoping Li
Haoran Xie
Qing Li
Wenyin Liu

Knowledge-based visual question answering (KB-VQA) requires reasoning about the visual grounding relations between the images and questions by incorporating external knowledge. Existing works typically retrieve knowledge from knowledge graphs by leveraging global multimodal representations of image–text pairs for graph convolution, which neglect contextual clues at hop granularity, resulting in suboptimal spreading and leveraging of contextual information. To this end, we propose a multi-hop graph reasoning network (MGRN) for KB-VQA, which consists of a knowledge graph constructor (KGC) module, a semantic-instructed graph reasoning (SGR) module, and an answering module. MGRN exploits multimodal semantics from given images and questions as instructions for graph reasoning to obtain the knowledge representation from either the scene graph or knowledge base. Specifically, KGC fuses the scene graph with triplets from ConceptNet and Comet to construct a contextual knowledge graph for retrieving knowledge representation. Furthermore, SGR conducts multi-hop graph reasoning to select top- K knowledge items for answering by passing and filtering interplay messages on contextual knowledge graphs under the guidance of multimodal semantic representation. Extensive experiments conducted on two public datasets show the effectiveness and outperformance of our method.

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ECAI Conference 2025 Conference Paper

Multi-Perspective Frequency Domain Learning for Generalizable AI-Generated Image Detection

Zili Xu
Jianjie Luo
Fuqiang Yu
Zhenguo Yang

The prevalence of generative models in image and video generation has raised extensive concerns about potential harm and misuse. To identify the truthfulness of generated images, most of the existing methods typically apply Fast Fourier Transform (FFT) for frequency extraction. An existing problem is that the frequency-domain representations extracted by FFT are not comprehensive for AI-generated image detection. In this paper, we propose a Multi-perspective Frequency Domain Learning (MFDL) framework, which aims to learn both generalized and discriminative frequency representations via DWT and FFT. Specifically, we design a Frequency Representation Enhancement (FRE) module using the Discrete Wavelet Transform (DWT) and incorporating a multi-granularity enhancement strategy that amplifies all subbands across high frequency to improve discriminability. Additionally, we introduce a Frequency Representation Consistency (FRC) module, which employs complex convolution to capture and preserve forgery patterns in the real and imaginary components derived from FFT. By integrating complementary frequency representations from the DWT and FFT domains obtained through the FRE and FRC modules, MFDL achieves a comprehensive understanding of forgery traces in the frequency domain. This enhances the model’s generalization capability for detecting generated content. Extensive experiments conducted on 32 distinct datasets, covering both GAN-generated and Diffusion-based images, demonstrate the effectiveness of our proposed MFDL framework. These experiments validate the effectiveness of multi-perspective frequency domain learning and show that MFDL outperforms existing detection methods, confirming its strong generalization ability across diverse generative models.

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NeurIPS Conference 2025 Conference Paper

PairEdit: Learning Semantic Variations for Exemplar-based Image Editing

Haoguang Lu
Jiacheng Chen
Zhenguo Yang
Aurele Gnanha
Fu Lee Wang
Qing Li
Xudong Mao

Recent advancements in text-guided image editing have achieved notable success by leveraging natural language prompts for fine-grained semantic control. However, certain editing semantics are challenging to specify precisely using textual descriptions alone. A practical alternative involves learning editing semantics from paired source-target examples. Existing exemplar-based editing methods still rely on text prompts describing the change within paired examples or learning implicit text-based editing instructions. In this paper, we introduce PairEdit, a novel visual editing method designed to effectively learn complex editing semantics from a limited number of image pairs or even a single image pair, without using any textual guidance. We propose a target noise prediction that explicitly models semantic variations within paired images through a guidance direction term. Moreover, we introduce a content-preserving noise schedule to facilitate more effective semantic learning. We also propose optimizing distinct LoRAs to disentangle the learning of semantic variations from content. Extensive qualitative and quantitative evaluations demonstrate that PairEdit successfully learns intricate semantics while significantly improving content consistency compared to baseline methods. Code is available at https: //github. com/xudonmao/PairEdit.

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IROS Conference 2021 Conference Paper

DepthGrasp: Depth Completion of Transparent Objects Using Self-Attentive Adversarial Network with Spectral Residual for Grasping

Yingjie Tang
Junhong Chen
Zhenguo Yang
Zehang Lin
Qing Li 0001
Wenyin Liu

Transparent objects with unique visual properties often make depth cameras fail to scan their reflective and refractive surfaces. Recent studies on depth completion of transparent objects have leveraged a linear system based on the geometric constraints to predict the missing depth, which is hard to be employed in an end-to-end framework and achieve joint optimization. In this paper, we propose DepthGrasp - a deep learning approach for depth completion of transparent objects from a raw RGB-D image. More specifically, we use a generative adversarial network, which utilizes the generator to complete the depth maps by predicting the missing or inaccurate depth values, and use discriminator to guide the completed depth maps against the groundtruth. In the generator, we devise spectral residual blocks (SRB) with spectral normalization for network stability, and residual block to pass the attention map in order to capture the structure information and distinguish the geometric shape of transparent objects. In the discriminator, we use a patch-based convolutional network to adapt the data distributions of the predicted depth maps according to groundtruth. Extensive experiments conducted on ClearGrasp dataset show the effectiveness and generalization of the DepthGrasp for depth completion, and the deployed robotic picking system makes significant improvement on the performance of grasping on transparent objects.

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