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Junfu Pu

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

Verb Mirage: Unveiling and Assessing Verb Concept Hallucinations in Multimodal Large Language Models

Zehao Wang
Xinpeng Liu
Yudonglin Zhang
Xiaoqian Wu
Zhou Fang
Yifan Fang
Junfu Pu
Cewu Lu

Multimodal Large Language Models (MLLMs) have garnered significant attention recently and demonstrate outstanding capabilities in various tasks such as OCR, VQA, captioning, etc. However, hallucination remains a persistent issue. While numerous methods have been proposed to mitigate hallucinations, achieving notable improvements, these methods primarily focus on mitigating hallucinations related to object/noun concepts. Verb concepts, which are crucial for understanding human actions, have been largely overlooked. In this paper, to the best of our knowledge, we are the first to investigate the verb hallucination phenomenon of MLLMs from various perspectives. Our findings reveal that most state-of-the-art MLLMs suffer from severe verb hallucination. To assess the effectiveness of existing mitigation methods for object concept hallucination in relation to verb hallucination, we evaluated these methods and found that they do not effectively address verb hallucination. To address this issue, we propose a baseline method based on fine-tuning with rich verb knowledge, achieving decent superiority. The experiment results demonstrate that our method significantly reduces hallucinations related to verbs.

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ICML Conference 2025 Conference Paper

Taming Rectified Flow for Inversion and Editing

Jiangshan Wang
Junfu Pu
Zhongang Qi
Jiayi Guo
Yue Ma 0016
Nisha Huang
Yuxin Chen
Xiu Li 0001

Rectified-flow-based diffusion transformers like FLUX and OpenSora have demonstrated outstanding performance in the field of image and video generation. Despite their robust generative capabilities, these models often struggle with inversion inaccuracies, which could further limit their effectiveness in downstream tasks such as image and video editing. To address this issue, we propose RF-Solver, a novel training-free sampler that effectively enhances inversion precision by mitigating the errors in the ODE-solving process of rectified flow. Specifically, we derive the exact formulation of the rectified flow ODE and apply the high-order Taylor expansion to estimate its nonlinear components, significantly enhancing the precision of ODE solutions at each timestep. Building upon RF-Solver, we further propose RF-Edit, a general feature-sharing-based framework for image and video editing. By incorporating self-attention features from the inversion process into the editing process, RF-Edit effectively preserves the structural information of the source image or video while achieving high-quality editing results. Our approach is compatible with any pre-trained rectified-flow-based models for image and video tasks, requiring no additional training or optimization. Extensive experiments across generation, inversion, and editing tasks in both image and video modalities demonstrate the superiority and versatility of our method. The source code is available at https: //github. com/wangjiangshan0725/RF-Solver-Edit.

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NeurIPS Conference 2025 Conference Paper

UniPixel: Unified Object Referring and Segmentation for Pixel-Level Visual Reasoning

Ye Liu
Zongyang Ma
Junfu Pu
Zhongang Qi
Yang Wu
Ying Shan
Chang Chen

Recent advances in Large Multi-modal Models (LMMs) have demonstrated their remarkable success as general-purpose multi-modal assistants, with particular focuses on holistic image- and video-language understanding. Conversely, less attention has been given to scaling fine-grained pixel-level understanding capabilities, where the models are expected to realize pixel-level alignment between visual signals and language semantics. Some previous studies have applied LMMs to related tasks such as region-level captioning and referring expression segmentation. However, these models are limited to performing either referring or segmentation tasks independently and fail to integrate these fine-grained perception capabilities into visual reasoning. To bridge this gap, we propose UniPixel, a large multi-modal model capable of flexibly comprehending visual prompt inputs and generating mask-grounded responses. Our model distinguishes itself by seamlessly integrating pixel-level perception with general visual understanding capabilities. Specifically, UniPixel processes visual prompts and generates relevant masks on demand, and performs subsequent reasoning conditioning on these intermediate pointers during inference, thereby enabling fine-grained pixel-level reasoning. The effectiveness of our approach has been verified on 10 benchmarks across a diverse set of tasks, including pixel-level referring/segmentation and object-centric understanding in images/videos. A novel PixelQA task that jointly requires referring, segmentation, and question answering is also designed to verify the flexibility of our method.

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IJCAI Conference 2018 Conference Paper

Dilated Convolutional Network with Iterative Optimization for Continuous Sign Language Recognition

Junfu Pu
Wengang Zhou
Houqiang Li

This paper presents a novel deep neural architecture with iterative optimization strategy for real-world continuous sign language recognition. Generally, a continuous sign language recognition system consists of visual input encoder for feature extraction and a sequence learning model to learn the correspondence between the input sequence and the output sentence-level labels. We use a 3D residual convolutional network (3D-ResNet) to extract visual features. After that, a stacked dilated convolutional network with Connectionist Temporal Classification (CTC) is applied for learning the mapping between the sequential features and the text sentence. The deep network is hard to train since the CTC loss has limited contribution to early CNN parameters. To alleviate this problem, we design an iterative optimization strategy to train our architecture. We generate pseudo-labels for video clips from sequence learning model with CTC, and fine-tune the 3D-ResNet with the supervision of pseudo-labels for a better feature representation. We alternately optimize feature extractor and sequence learning model with iterative steps. Experimental results on RWTH-PHOENIX-Weather, a large real-world continuous sign language recognition benchmark, demonstrate the advantages and effectiveness of our proposed method.

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