13 citations · 22 across the 12 of their papers we have counts for
13 papers
StepGuard: Guarding Web Navigation via Single-Step Calibration
Zhihao Cui, Yuchen Zhang, Xiyang Sun +6
Web navigation requires agents to follow natural language goals, interact with web pages, and produce accurate answers. While recent advances leverage vision-language models and re…
CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection
Jinjie Shen, Yaxiong Wang, Yujiao Wu +5
The rapid rise of generative AI has made multimodal fake news increasingly realistic and pervasive, posing severe threats to public trust and social stability. Existing detection m…
OmniVL-Guard Pro: A Tool-Augmented Agent for Omnibus Vision-Language Forensics
Jinjie Shen, Zheng Huang, Yuchen Zhang +7
Existing vision-language forgery detection and grounding methods operate under a closed-world paradigm, assuming verification can be completed by the model alone. However, self-con…
Efficient Encoder-Free Fourier-based 3D Large Multimodal Model
Guofeng Mei, Wei Lin, Luigi Riz +3
Large Multimodal Models (LMMs) that process 3D data typically rely on heavy, pre-trained visual encoders to extract geometric features. While recent 2D LMMs have begun to eliminate…
Cultivating Forensic Reasoning for Generalizable Multimodal Manipulation Detection
Yuchen Zhang, Yaxiong Wang, Kecheng Han +4
Recent advances in generative AI have significantly enhanced the realism of multimodal media manipulation, thereby posing substantial challenges to manipulation detection. Existing…
The Coherence Trap: When MLLM-Crafted Narratives Exploit Manipulated Visual Contexts
Yuchen Zhang, Yaxiong Wang, Yujiao Wu +3
The detection and grounding of multimedia manipulation has emerged as a critical challenge in combating AI-generated disinformation. While existing methods have made progress in re…
Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline
Jingchun Lian, Lingyu Liu, Yaxiong Wang +4
Existing facial forgery detection methods typically focus on binary classification or pixel-level localization, providing little semantic insight into the nature of the manipulatio…
CaLa: Complementary Association Learning for Augmenting Composed Image Retrieval
Xintong Jiang, Yaxiong Wang, Mengjian Li +3
Composed Image Retrieval (CIR) involves searching for target images based on an image-text pair query. While current methods treat this as a query-target matching problem, we argue…
Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class Discovery
Ye Wang, Yaxiong Wang, Yujiao Wu +2
Generalized Class Discovery (GCD) aims to dynamically assign labels to unlabelled data partially based on knowledge learned from labelled data, where the unlabelled data may come f…
POS: A Prompts Optimization Suite for Augmenting Text-to-Video Generation
Shijie Ma, Huayi Xu, Mengjian Li +3
This paper targets to enhance the diffusion-based text-to-video generation by improving the two input prompts, including the noise and the text. Accommodated with this goal, we pro…
Dual Relation Alignment for Composed Image Retrieval
Xintong Jiang, Yaxiong Wang, Yujiao Wu +2
Composed image retrieval, a task involving the search for a target image using a reference image and a complementary text as the query, has witnessed significant advancements owing…
Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark
Shuyu Yang, Yinan Zhou, Yaxiong Wang +3
In this paper, we introduce a large Multi-Attribute and Language Search dataset for text-based person retrieval, called MALS, and explore the feasibility of performing pre-training…
AINet+: Advancing Superpixel Segmentation via Cascaded Association Implantation
Yaxiong Wang, Yunchao Wei, Yujiao Wu +3
Superpixel segmentation has seen significant progress benefiting from the deep convolutional networks. The typical approach entails initial division of the image into grids, follow…