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- Yu‐Jin ZhangSuggested from co-authorship
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Works281 from public data
- Robust Text Detection in Natural Scene Images662
An accurate and robust method for detecting texts in natural scene images using a fast and effective pruning algorithm to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations is proposed.
- OCRBench: on the hidden mystery of OCR in large multimodal models574
A comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks, revealing both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression recognition.
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- Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection255
This paper proposes a novel unified relational reasoning graph network for arbitrary shape text detection through an innovative local graph that bridges a text proposal model via Convolutional Neural Network and a deep relational reasoning network via Graphconvolutional Network, making the network end-to-end trainable.
- A Short Survey of Recent Advances in Graph Matching223
The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.
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- Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection138
A novel adaptive boundary proposal network for arbitrary shape text detection, which can learn to directly produce accurate boundary for arbitraryshape text without any post-processing, is proposed.
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- Arbitrary Shape Text Detection via Boundary Transformer80
A unified coarse-to-fine framework via boundary learning for arbitrary shape text detection, which can accurately and efficiently locate text boundaries without post-processing, and proposes a novel boundary energy loss that introduces an energy minimization constraint and an energy monotonically decreasing constraint to further optimize and stabilize the learning of boundary refinement.
- Arbitrary Shape Text Detection via Segmentation with Probability Maps64
This paper proposes an innovative and robust segmentation-based detection method via probability maps for accurately detecting text instances through a group of probability maps computed by a series of Sigmoid Alpha Functions to describe the possible probability distributions.
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- Feature combination using boosting58
Experiments on classification of three UCI data sets and handwritten digit recognition indicate that this variant of boosting based feature combination is a promising learning algorithm for feature combination.
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- VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision55
A novel approach via Vision-Language semantic self-supervision for context-aware Pedestrian Detection (VLPD) to model explicitly semantic contexts without any extra annotations is proposed and achieves superior performances over the previous state-of-the-arts.
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- Convex ensemble learning with sparsity and diversity48
This paper forms the classifier ensemble problem with the sparsity and diversity learning in a general mathematical framework, and designs a convex ensemble diversity measure, derived from the error-ambiguity decomposition.
- Kernel Proposal Network for Arbitrary Shape Text Detection47
This work is the first to introduce the dynamic convolution kernel strategy to efficiently and effectively tackle the adhesion problem of neighboring text instances in text detection and improve the robustness against unclear boundaries.
- DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework46
This paper proposes DPFlow, an adaptive optical flow architecture capable of generalizing up to 8K resolution inputs while trained with only low-resolution samples, and introduces Kubric-NK, a new benchmark for evaluating optical flow methods with input resolutions ranging from 1K to 8K.
- A novel classifier ensemble method with sparsity and diversity46
This work forms the classifier ensemble problem with the sparsity or/and diversity learning in a general framework, and proposes a heuristic algorithm, capable of obtaining ensemble classifiers with consideration of both sparsity and diversity.
- LSTM $$^{2}$$ 2 : Multi-Label Ranking for Document Classification45
An Long Short Term Memory-based multi-label ranking model for document classification, namely LSTM$$^2$$2 consisting of repLSTM—an adaptive data representation process and rankL STM—a unified learning-ranking process is proposed.
- RUArt: A Novel Text-Centered Solution for Text-Based Visual Question Answering44
Experimental results demonstrate that the proposed RUArt (Reading, Understanding and Answering the Related Text) method can effectively explore the contextual information of the text and mine the stable relationships between theText and objects.
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- Recurrent Partial Kernel Network for Efficient Optical Flow Estimation38
This paper designs a new spatial recurrent encoder that extracts discriminative features at a significantly reduced size and utilizes Partial Kernel Convolution (PKConv) layers to produce variable multi-scale features with a single shared block.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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