Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks94 from public data
- PolarMask: Single Shot Instance Segmentation With Polar Representation641
This paper introduces an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used by easily embedding it into most off-the-shelf detection methods.
- FOTS: Fast Oriented Text Spotting with a Unified Network539
This work proposes a unified end-to-end trainable Fast Oriented Text Spotting (FOTS) network for simultaneous detection and recognition, sharing computation and visual information among the two complementary tasks, and introduces RoIRotate to share convolutional features between detection and Recognition.
- Scene Text Image Super-Resolution in the Wild186
A new Text Super-Resolution Network termed TSRN, with three novel modules is developed, which improves the recognition accuracy by over 13% of CRNN, and by nearly 9.0% of ASTER and MORAN compared to synthetic SR data.
- PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text177
An end-to-end text spotting framework, termed PAN++, which can efficiently detect and recognize text of arbitrary shapes in natural scenes, based on the kernel representation that reformulates a text line as a text kernel surrounded by peripheral pixels.
- Norm-Based Curriculum Learning for Neural Machine Translation135
This paper aims to improve the efficiency of training an NMT by introducing a novel norm-based curriculum learning method that uses the norm (aka length or module) of a word embedding as a measure of the difficulty of the sentence, the competence of the model, and the weight of the sentences.
- Parameter-Efficient and Student-Friendly Knowledge Distillation90
Experiments on various benchmarks including CV and NLP show that PESF-KD can significantly reduce the training cost while obtaining competitive results compared to advanced online distillation methods.
- 78
- Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation55
This work directly exposes the raw data into NAT by leveraging pretraining to rejuvenate more alignments for low-frequency target words, and combines these complementary approaches as a new training strategy for further boosting NAT performance.
- Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation51
Experimental results on 10 different low-resource domains show that meta-curriculum learning can improve the translation performance of both familiar and unfamiliar domains.
- 50
- DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding48
This work utilizes the state-of-the-art models and design targeted extraction modules to extract multimodal features from semantic contents, layout information, and visual images, and adopts an asymmetric algorithm and negative sampling in the model.
- Progressive Multi-Granularity Training for Non-Autoregressive Translation47
This study empirically shows that NAT models are prone to learn fine-grained lower-mode knowledge, such as words and phrases, compared with sentences, and proposes progressive multi-granularity training for NAT, which improves phrase translation accuracy and model reordering ability, resulting in better translation quality against strong NAT baselines.
- 47
- Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore45
This paper proposes a simple yet effective black-box zero-shot detection approach based on the observation that, from the perspective of LLMs, human-written texts typically contain more grammatical errors than LLM-generated texts.
- Can LLMs Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner41
An uncertainty-aware instruction tuning (UaIT) method is proposed, aligning LLMs’ perception with the probabilistic uncertainty of the generation to instigate a heightened awareness of self-uncertainty in LLMs, enabling them to express uncertainty more effectively.
- DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memory38
Results indicate that DelTA significantly outperforms strong baselines in terms of translation consistency and quality across four open/closed-source LLMs and two representative document translation datasets, achieving an increase in consistency scores by up to 4.58 percentage points and in COMET scores by up to 3.16 points on average.
- Shared-Private Bilingual Word Embeddings for Neural Machine Translation38
Experiments demonstrate that the proposed shared-private bilingual word embeddings provide a significant performance boost over the strong baselines with dramatically fewer model parameters.
- 38
- TemplateGEC: Improving Grammatical Error Correction with Detection Template34
A novel method, TemplateGEC, is proposed, which capitalizes on the capabilities of both Seq2Edit and Sequ2Seq frameworks in error detection and correction respectively, and reveals the potential of the method in performing human-in-the-loop GEC.
- 34
- 29
- On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation29
Experimental results show that PT and BT are nicely complementary to each other, establishing state-of-the-art performances on the WMT16 English-Romanian and English-Russian benchmarks.
- AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting28
This work proposes a novel text spotter, named Ambiguity Eliminating Text Spotter (AE TextSpotter), which learns both visual and linguistic features to significantly reduce ambiguity in text detection, and is the first time to improve text detection by using a language model.
- ZhihuRank: A Topic-Sensitive Expert Finding Algorithm in Community Question Answering Websites28
A topic-sensitive probabilistic model to estimate the user authority ranking for each question is proposed, based on the link analysis technique and topical similarities between users and questions, which is more effective than existing approaches.
- Knowledge Editing with Dynamic Knowledge Graphs for Multi-Hop Question Answering26
KEDKG is introduced, a novel knowledge editing method that leverages a dynamic knowledge graph for MHQA, designed to ensure the reliability of answers and surpasses previous state-of-the-art models, delivering more accurate and reliable answers in environments with dynamic information.
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 2026-10-15. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.