50 citations · 72 across the 18 of their papers we have counts for
20 papers
VTAgent: Agentic Keyframe Anchoring for Evidence-Aware Video TextVQA
Haibin He, Maoyuan Ye, Jing Zhang +2
Video text-based visual question answering (Video TextVQA) aims to answer questions by reasoning over visual textual content appearing in videos. Despite the strong multimodal vide…
Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing
Cheng Cui, Ting Sun, Suyin Liang +15
Document parsing is a fine-grained task where image resolution significantly impacts performance. While advanced research leveraging vision-language models benefits from high-resol…
PP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Tasks
Cheng Cui, Yubo Zhang, Ting Sun +11
The advent of "OCR 2.0" and large-scale vision-language models (VLMs) has set new benchmarks in text recognition. However, these unified architectures often come with significant c…
Heuristic-inspired Reasoning Priors Facilitate Data-Efficient Referring Object Detection
Xu Zhang, Zhe Chen, Jing Zhang +1
Most referring object detection (ROD) models, especially the modern grounding detectors, are designed for data-rich conditions, yet many practical deployments, such as robotics, au…
SFA: Scan, Focus, and Amplify toward Guidance-aware Answering for Video TextVQA
Haibin He, Qihuang Zhong, Juhua Liu +3
Video text-based visual question answering (Video TextVQA) task aims to answer questions about videos by leveraging the visual text appearing within the videos. This task poses sig…
Rethink Sparse Signals for Pose-guided Text-to-image Generation
Wenjie Xuan, Jing Zhang, Juhua Liu +2
Recent works favored dense signals (e.g., depth, DensePose), as an alternative to sparse signals (e.g., OpenPose), to provide detailed spatial guidance for pose-guided text-to-imag…
Reasoning-OCR: Can Large Multimodal Models Solve Complex Logical Reasoning Problems from OCR Cues?
Haibin He, Maoyuan Ye, Jing Zhang +4
Large Multimodal Models (LMMs) have become increasingly versatile, accompanied by impressive Optical Character Recognition (OCR) related capabilities. Existing OCR-related benchmar…
GoMatching++: Parameter- and Data-Efficient Arbitrary-Shaped Video Text Spotting and Benchmarking
Haibin He, Jing Zhang, Maoyuan Ye +3
Video text spotting (VTS) extends image text spotting (ITS) by adding text tracking, significantly increasing task complexity. Despite progress in VTS, existing methods still fall…
LogicOCR: Do Your Large Multimodal Models Excel at Logical Reasoning on Text-Rich Images?
Maoyuan Ye, Haibin He, Qihuang Zhong +3
Recent advances in Large Multimodal Models (LMMs) have revolutionized their reasoning and Optical Character Recognition (OCR) capabilities. However, their complex logical reasoning…
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
Di Wang, Jing Zhang, Minqiang Xu +8
Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervis…
Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation
Maoyuan Ye, Jing Zhang, Juhua Liu +5
The Segment Anything Model (SAM), a profound vision foundation model pretrained on a large-scale dataset, breaks the boundaries of general segmentation and sparks various downstrea…
GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching
Haibin He, Maoyuan Ye, Jing Zhang +3
Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-en…
Multi-Granularity Hand Action Detection
Ting Zhe, Jing Zhang, Yongqian Li +3
Detecting hand actions in videos is crucial for understanding video content and has diverse real-world applications. Existing approaches often focus on whole-body actions or coarse…
Scalable Mask Annotation for Video Text Spotting
Haibin He, Jing Zhang, Mengyang Xu +3
Video text spotting refers to localizing, recognizing, and tracking textual elements such as captions, logos, license plates, signs, and other forms of text within consecutive vide…
DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting
Maoyuan Ye, Jing Zhang, Shanshan Zhao +4
End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal ro…
SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model
Di Wang, Jing Zhang, Bo Du +4
The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with anno…
DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting
Maoyuan Ye, Jing Zhang, Shanshan Zhao +4
End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal ro…
DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer
Maoyuan Ye, Jing Zhang, Shanshan Zhao +3
Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods b…
Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition
Yue He, Chen Chen, Jing Zhang +4
Existing Scene Text Recognition (STR) methods typically use a language model to optimize the joint probability of the 1D character sequence predicted by a visual recognition (VR) m…
I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection
Bo Du, Jian Ye, Jing Zhang +2
Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i.e., 1) fracture detections at the gaps in a text instance; and 2) inaccurate dete…