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Wenqing Zhang

14 accepted papers

2026

DeTri: Debiasing General-Purpose LLMs for Zero-Shot Relation Triplet Extraction via Structural Expert

IJCAI 2026

Zero-Shot Relation Triplet Extraction (ZSRTE) aims to extract relation triplets for unseen relation types without any annotated training data. Recent advancements in Large Language Models (LLMs) have significantly enhanced ZSRTE performance, enabling the direct generation of relational triplets from

Cited by 0Scholar
2025

CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models

ICLR 2025poster

Virtual try-on methods based on diffusion models achieve realistic effects but often require additional encoding modules, a large number of training parameters, and complex preprocessing, which increases the burden on training and inference. In this work, we re-evaluate the necessity of additional m…

2025

Frame First, Then Extract: A Frame-Semantic Reasoning Pipeline for Zero-Shot Relation Triplet Extraction

EMNLP 2025

Large Language Models (LLMs) have shown impressive capabilities in language understanding and generation, leading to growing interest in zero-shot relation triplet extraction (ZeroRTE), a task that aims to extract triplets for unseen relations without annotated data. However, existing methods typica

Cited by 0SourcePDFScholar
2024

DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing

CVPR 2024highlight

Accurate and controllable image editing is a challenging task that has attracted significant attention recently. Notably DragGAN developed by Pan et al. (2023) is an interactive point-based image editing framework that achieves impressive editing results with pixel-level precision. However due to it…

2024

Free-ATM: Harnessing Free Attention Masks for Representation Learning on Diffusion-Generated Images

ECCV 2024poster

"This paper studies visual representation learning with diffusion-generated synthetic images. We start by uncovering that diffusion models’ cross-attention layers inherently provide annotation-free attention masks aligned with corresponding text inputs on generated images. We then investigate the pr…

2023

DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment

ICCV 2023poster

Cross-modal garment synthesis and manipulation will significantly benefit the way fashion designers generate garments and modify their designs via flexible linguistic interfaces. However, despite the significant progress that has been made in generic image synthesis using diffusion models, producing…

Cited by 17PDFScholar
2023

IS SYNTHETIC DATA FROM GENERATIVE MODELS READY FOR IMAGE RECOGNITION?

ICLR 2023top-25%

Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images. Though the results are astonishing to human eyes, how applicable these generated images are for recognition tasks remains under-explored. In this work, we extensively study whether…

2023

PLA: Language-Driven Open-Vocabulary 3D Scene Understanding

CVPR 2023poster

Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space. The recent breakthrough of 2D open-vocabulary perception is largely driven by Internet-scale paired image-text data with rich vocabulary concepts. However, this success cannot be di…

2023

PV3D: A 3D Generative Model for Portrait Video Generation

ICLR 2023poster

Recent advances in generative adversarial networks (GANs) have demonstrated the capabilities of generating stunning photo-realistic portrait images. While some prior works have applied such image GANs to unconditional 2D portrait video generation and static 3D portrait synthesis, there are few works…

2023

Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning

ICLR 2023poster

Federated learning aims to train models collaboratively across different clients without sharing data for privacy considerations. However, one major challenge for this learning paradigm is the data heterogeneity problem, which refers to the discrepancies between the local data distributions among va…

2022

Contextual Text Block Detection towards Scene Text Understanding

ECCV 2022poster

"Most existing scene text detectors focus on detecting characters or words that only capture partial text messages due to missing contextual information. For a better understanding of text in scenes, it is more desired to detect contextual text blocks (CTBs) which consist of one or multiple integral…

2022

Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection

CVPR 2022poster

Recently, transformer-based methods have achieved promising progresses in object detection, as they can eliminate the post-processes like NMS and enrich the deep representations. However, these methods cannot well cope with scene text due to its extreme variance of scales and aspect ratios. In this…

Cited by 121PDFScholar
2022

Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spotting

ECCV 2022poster

"Recently, Vision-Language Pre-training (VLP) techniques have greatly benefited various vision-language tasks by jointly learning visual and textual representations, which intuitively helps in Optical Character Recognition (OCR) tasks due to the rich visual and textual information in scene text imag…

Cited by 43SourcePDFScholar
2022

SeqFormer: Sequential Transformer for Video Instance Segmentation

ECCV 2022poster

"In this work, we present SeqFormer for video instance segmentation. SeqFormer follows the principle of vision transformer that models instance relationships among video frames. Nevertheless, we observe that a stand-alone instance query suffices for capturing a time sequence of instances in a video,…