87 citations · 319 across the 30 of their papers we have counts for
31 papers
BrowserForge: Scaling Web Episode via Parallel Browser Sandboxes
Fei Tang, Huawen Shen, Zhiqiong Lu +7
Web agents that act from rendered pixels avoid the fragility and heavy token cost of reading a page's HTML or accessibility tree, but training them depends on large amounts of high…
HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better
Gengluo Li, Xingyu Wan, Shangpin Peng +20
We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, information extraction, text-image tr…
HyMobileAgent: Data-Environment Co-Scaling for Efficient GUI Agents
Hy Vision Team, Huawen Shen, Zhengyang Tang +20
As large multimodal models move from understanding content to operating on digital environments, mobile GUI has emerged as a challenging and consequential testbed for digital embod…
StrucTab: A Structured Optimization Framework for Table Parsing
Gengluo Li, Shangpin Peng, Chengquan Zhang +10
Table parsing aims to convert table images into structured, machine-readable representations, a task requiring the joint perception of complex spatial layouts and textual content.…
PhoneBuddy: Training Open Models for Agentic Phone Use
Zhengyang Tang, Xin Lai, Pengyuan Lyu +23
Phones are becoming an important execution surface for general-purpose agents, but training open models for reliable phone use remains difficult because the environment that matter…
PhoneHarness: Harnessing Phone-Use Agents through Mixed GUI, CLI, and Tool Actions
Chenxin Li, Zhengyao Fang, Zhengyang Tang +18
Phone agents are increasingly expected to complete real mobile workflows rather than merely predict the next screen action. However, much of the current mobile-agent literature sti…
Safe, or Simply Incapable? Rethinking Safety Evaluation for Phone-Use Agents
Zhengyang Tang, Yi Zhang, Chenxin Li +18
When a phone-use agent avoids harm, does that show safety, or simply inability to act? Existing evaluations often cannot tell. A harmful outcome may be avoided because the agent re…
PhoneWorld: From Real-App Trajectories to Dynamic and Verifiable Environments for Phone-Use Agents
Yuxuan Liu, Xin Lai, Junyi Li +21
Real applications provide the training setting closest to phone-agent deployment, but are difficult to reset, scale safely, and verify programmatically. Static screenshots and inte…
Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training
Gengluo Li, Pengyuan Lyu, Chengquan Zhang +7
Document parsing has recently advanced with multimodal large language models (MLLMs) that directly map document images to structured outputs. Traditional cascaded pipelines depend…
Do Phone-Use Agents Respect Your Privacy?
Zhengyang Tang, Ke Ji, Xidong Wang +19
We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compliant behavior is not operatio…
MMTIT-Bench: A Multilingual and Multi-Scenario Benchmark with Cognition-Perception-Reasoning Guided Text-Image Machine Translation
Gengluo Li, Chengquan Zhang, Yupu Liang +9
End-to-end text-image machine translation (TIMT), which directly translates textual content in images across languages, is crucial for real-world multilingual scene understanding.…
Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity
Zhengyao Fang, Pengyuan Lyu, Chengquan Zhang +3
Vision-language models (VLMs) face significant computational inefficiencies caused by excessive generation of visual tokens. While prior work shows that a large fraction of visual…
HunyuanOCR Technical Report
Hunyuan Vision Team, Pengyuan Lyu, Xingyu Wan +29
This paper presents HunyuanOCR, a commercial-grade, open-source, and lightweight (1B parameters) Vision-Language Model (VLM) dedicated to OCR tasks. The architecture comprises a Na…
Recognition-Synergistic Scene Text Editing
Zhengyao Fang, Pengyuan Lyu, Jingjing Wu +4
Scene text editing aims to modify text content within scene images while maintaining style consistency. Traditional methods achieve this by explicitly disentangling style and conte…
WeCromCL: Weakly Supervised Cross-Modality Contrastive Learning for Transcription-only Supervised Text Spotting
Jingjing Wu, Zhengyao Fang, Pengyuan Lyu +4
Transcription-only Supervised Text Spotting aims to learn text spotters relying only on transcriptions but no text boundaries for supervision, thus eliminating expensive boundary a…
StrucTexTv3: An Efficient Vision-Language Model for Text-rich Image Perception, Comprehension, and Beyond
Pengyuan Lyu, Yulin Li, Hao Zhou +8
Text-rich images have significant and extensive value, deeply integrated into various aspects of human life. Notably, both visual cues and linguistic symbols in text-rich images pl…
Towards Unified Multi-granularity Text Detection with Interactive Attention
Xingyu Wan, Chengquan Zhang, Pengyuan Lyu +5
Existing OCR engines or document image analysis systems typically rely on training separate models for text detection in varying scenarios and granularities, leading to significant…
GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction
Pengyuan Lyu, Weihong Ma, Hongyi Wang +5
All tables can be represented as grids. Based on this observation, we propose GridFormer, a novel approach for interpreting unconstrained table structures by predicting the vertex…
Towards Robust Real-Time Scene Text Detection: From Semantic to Instance Representation Learning
Xugong Qin, Pengyuan Lyu, Chengquan Zhang +5
Due to the flexible representation of arbitrary-shaped scene text and simple pipeline, bottom-up segmentation-based methods begin to be mainstream in real-time scene text detection…
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
Wenwen Yu, Chengquan Zhang, Haoyu Cao +24
Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, an…
Single Shot Self-Reliant Scene Text Spotter by Decoupled yet Collaborative Detection and Recognition
Jingjing Wu, Pengyuan Lyu, Guangming Lu +2
Typical text spotters follow the two-stage spotting paradigm which detects the boundary for a text instance first and then performs text recognition within the detected regions. De…
MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining
Pengyuan Lyu, Chengquan Zhang, Shanshan Liu +7
Text images contain both visual and linguistic information. However, existing pre-training techniques for text recognition mainly focus on either visual representation learning or…
PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network
Pengfei Wang, Chengquan Zhang, Fei Qi +7
The reading of arbitrarily-shaped text has received increasing research attention. However, existing text spotters are mostly built on two-stage frameworks or character-based metho…
Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
Minghui Liao, Pengyuan Lyu, Minghang He +3
Unifying text detection and text recognition in an end-to-end training fashion has become a new trend for reading text in the wild, as these two tasks are highly relevant and compl…