arXiv subjects

Jinxi Liu

Publications and source records attributed to Jinxi Liu.

3 recordsLinked to original sources

arXiv2025

One Model for All: Unified Try-On and Try-Off in Any Pose via LLM-Inspired Bidirectional Tweedie Diffusion

Recent diffusion-based approaches have made significant advances in image-based virtual try-on, enabling more realistic and end-to-end garment synthesis. However, most existing methods remain constrained by their reliance on exhibition garments and segmentation masks, as well as their limited ability to handle flexible pose variations. These limitations reduce their practicality in real-world scenarios; for instance, users cannot easily transfer garments worn by one person onto another, and the generated try-on results are typically restricted to the same pose as the reference image. In this paper, we introduce OMFA (One Model For All), a unified diffusion framework for both virtual try-on and try-off that operates without the need for exhibition garments and supports arbitrary poses. OMFA is inspired by the mask-based paradigm of discrete diffusion language models and unifies try-on and try-off within a bidirectional framework. It is built upon a Bidirectional Tweedie Diffusion process for target-selective denoising in latent space. Instead of imposing lower body constraints, OMFA is an entirely mask-free framework that requires only a single portrait and a target garment as inputs, and is designed to support flexible outfit combinations and cross-person garment transfer, making it better aligned with practical usage scenarios. Additionally, by leveraging SMPL-X-based pose conditioning, OMFA supports multi-view and arbitrary-pose try-on from just one image. Extensive experiments demonstrate that OMFA achieves state-of-the-art results on both try-on and try-off tasks, providing a practical and generalizable solution for virtual garment synthesis. Project page: https://onemodelforall.github.io

cs.CV↗

arXiv2023

Causality-aware Concept Extraction based on Knowledge-guided Prompting

Concepts benefit natural language understanding but are far from complete in existing knowledge graphs (KGs). Recently, pre-trained language models (PLMs) have been widely used in text-based concept extraction (CE). However, PLMs tend to mine the co-occurrence associations from massive corpus as pre-trained knowledge rather than the real causal effect between tokens. As a result, the pre-trained knowledge confounds PLMs to extract biased concepts based on spurious co-occurrence correlations, inevitably resulting in low precision. In this paper, through the lens of a Structural Causal Model (SCM), we propose equipping the PLM-based extractor with a knowledge-guided prompt as an intervention to alleviate concept bias. The prompt adopts the topic of the given entity from the existing knowledge in KGs to mitigate the spurious co-occurrence correlations between entities and biased concepts. Our extensive experiments on representative multilingual KG datasets justify that our proposed prompt can effectively alleviate concept bias and improve the performance of PLM-based CE models.The code has been released on https://github.com/siyuyuan/KPCE.

cs.CL↗

arXiv2022

Generative Entity Typing with Curriculum Learning

Entity typing aims to assign types to the entity mentions in given texts. The traditional classification-based entity typing paradigm has two unignorable drawbacks: 1) it fails to assign an entity to the types beyond the predefined type set, and 2) it can hardly handle few-shot and zero-shot situations where many long-tail types only have few or even no training instances. To overcome these drawbacks, we propose a novel generative entity typing (GET) paradigm: given a text with an entity mention, the multiple types for the role that the entity plays in the text are generated with a pre-trained language model (PLM). However, PLMs tend to generate coarse-grained types after fine-tuning upon the entity typing dataset. Besides, we only have heterogeneous training data consisting of a small portion of human-annotated data and a large portion of auto-generated but low-quality data. To tackle these problems, we employ curriculum learning (CL) to train our GET model upon the heterogeneous data, where the curriculum could be self-adjusted with the self-paced learning according to its comprehension of the type granularity and data heterogeneity. Our extensive experiments upon the datasets of different languages and downstream tasks justify the superiority of our GET model over the state-of-the-art entity typing models. The code has been released on https://github.com/siyuyuan/GET.

cs.CL↗