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- BMN: Boundary-Matching Network for Temporal Action Proposal Generation749
This work proposes an effective, efficient and end-to-end proposal generation method, named Boundary-Matching Network (BMN), which generates proposals with precise temporal boundaries as well as reliable confidence scores simultaneously, and can achieve state-of-the-art temporal action detection performance.
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- AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer456
A novel attention and normalization module, named Adaptive Attention Normalization (AdaAttN), to adaptively perform attentive normalization on per-point basis is proposed, which achieves state-of-the-art arbitrary image/video style transfer.
- Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition426
A novel attention-based convolutional neural network (CNN) which regulates multiple object parts among different input images, which can be easily trained end-to-end, and is highly efficient which requires only one training stage.
- Student-Teacher Feature Pyramid Matching for Anomaly Detection396
This paper proposes a simple yet powerful approach to anomaly detection, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both accuracy and efficiency.
- Towards Accurate Scene Text Recognition With Semantic Reasoning Networks384
This work proposes a novel end-to-end trainable framework named semantic reasoning network (SRN) for accurate scene text recognition, where a global semantic reasoning module (GSRM) is introduced to capture global semantic context through multi-way parallel transmission.
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- ACFNet: Attentional Class Feature Network for Semantic Segmentation315
This paper presents the concept of class center which extracts the global context from a categorical perspective, and proposes a novel module, named Attentional Class Feature (ACF) module, to calculate and adaptively combine different class centers according to each pixel.
- Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes302
A novel text detector namely LOMO is presented, which localizes the text progressively for multiple times (or in other word, LOok More than Once), and the state-of-the-art results on several public benchmarks confirm the striking robustness and effectiveness of LomO.
- Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment283
Group DETR is introduced, a simple yet efficient DETR training approach that introduces a group-wise way for one-to-many assignment that speeds up the training convergence and improves the performance of various DETR-based models.
- Image Inpainting With Learnable Bidirectional Attention Maps282
This paper presents a learnable attention map module for learning feature re-normalization and mask-updating in an end-to-end manner, which is effective in adapting to irregular holes and propagation of convolution layers.
- ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text - RRC-ArT272
This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text - RRC-ArT that consists of three major challenges: i) scene text detection, ii) sceneText recognition, and iii) scenetext spotting.
- A Mutual Learning Method for Salient Object Detection With Intertwined Multi-Supervision255
This work proposes to train saliency detection networks by exploiting the supervision from not only salient object detection, but also foreground contour detection and edge detection, and develops a novel mutual learning module (MLM), which improves the performance by a large margin.
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- Dynamic Instance Normalization for Arbitrary Style Transfer222
The proposed Dynamic Instance Normalization (DIN) provides flexible support for state-of-the-art convolutional operations, and thus triggers novel functionalities, such as uniform-stroke placement for non-natural images and automatic spatial-stroke control.
- Perspective-Guided Convolution Networks for Crowd Counting217
A novel perspective-guided convolution (PGC) for convolutional neural network (CNN) based crowd counting (i.e. PGCNet) is proposed, which aims to overcome the dramatic intra-scene scale variations of people due to the perspective effect.
- WordSup: Exploiting Word Annotations for Character Based Text Detection209
A weakly supervised framework that can utilize word annotations, either in tight quadrangles or the more loose bounding boxes, for character detector training is proposed, able to train a robust character detector by exploiting word annotations in the rich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text.
- ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling - RRC-LSVT207
This competition aims to explore the abilities of state-of-the-art methods to detect and recognize text instances from large-scale street view images, closing gaps between research benchmarks and real applications.
- Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task182
The first high-diversity challenging Roadside Perception 3D dataset- Rope3D from a novel view is presented, which tailor the existing frontal-view monocular 3D object detection approaches and propose to leverage the geometry constraint to solve the inherent ambiguities caused by various sensors, viewpoints.
- Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement177
A novel framework that generates textured surface meshes from images by efficiently initializing the geometry and view-dependency decomposed appearance with a NeRF and adapting both vertex positions and face density based on reprojected rendering errors is presented.
- Editing Text in the Wild171
This work proposes an end-to-end trainable style retention network (SRNet) that consists of three modules: text conversion module, background inpainting module and fusion module, which is the first attempt to edit text in natural images at the word level.
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- StrucTexT: Structured Text Understanding with Multi-Modal Transformers157
This paper proposes a unified framework named StrucTexT, which is flexible and effective for handling both sub-tasks, and introduces a segment-token aligned encoder to deal with the entity labeling and entity linking tasks at different levels of granularity.
- MixFormer: Mixing Features across Windows and Dimensions152
This work combines local-window self-attention with depth-wise convolution in a parallel design, modeling cross-window connections to enlarge the receptive fields and proposes bi-directional interactions across branches to provide complementary clues in the channel and spatial dimensions.
- DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features148
This paper proposes a Deep Orthogonal Local and Global (DOLG) information fusion framework for end-to-end image retrieval that achieves state-of-the-art image retrieval performances on Revisited Oxford and Paris datasets.
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 2026-10-13. 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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