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- FCOS: Fully Convolutional One-Stage Object Detection6,481
For the first time, a much simpler and flexible detection framework achieving improved detection accuracy is demonstrated, and it is hoped that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks.
- Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling1,813
This work explores the relationship between model scaling and performance, systematically exploring the performance trends in vision encoders, language models, dataset sizes, and test-time configurations and hopes this model contributes to the open-source community by setting new standards for developing and applying multimodal AI systems.
- ResNeSt: Split-Attention Networks1,789
This work designs a new variant of the ResNet model, named ResNeSt, which outperforms EfficientNet in terms of the accuracy/latency trade-off and applies channel-wise attention across different network branches to leverage the complementary strengths of both feature-map attention and multi-path representation.
- Bag of Tricks for Image Classification with Convolutional Neural Networks1,654
This paper examines a collection of training procedure refinements and empirically evaluates their impact on the final model accuracy through ablation study, and shows that by combining these refinements together, they are able to improve various CNN models significantly.
- Detecting Text in Natural Image with Connectionist Text Proposal Network1,104
A novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image and develops a vertical anchor mechanism that jointly predicts location and text/non-text score of each fixed-width proposal, considerably improving localization accuracy.
- FCOS: A Simple and Strong Anchor-free Object Detector1,044
A fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to other dense prediction problems such as semantic segmentation, achieving improved detection accuracy.
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- ABCNet: Real-Time Scene Text Spotting With Adaptive Bezier-Curve Network462
For the first time, a novel BezierAlign layer is designed for extracting accurate convolution features of a text instance with arbitrary shapes, significantly improving the precision compared with previous methods and introducing negligible computation overhead.
- Text-Attentional Convolutional Neural Network for Scene Text Detection362
A new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components and a powerful low-level detector called contrast-enhancement maximally stable extremal regions (MSERs) is developed.
- Single Shot Text Detector with Regional Attention336
A novel single-shot text detector that directly outputs word-level bounding boxes in a natural image and develops a hierarchical inception module which efficiently aggregates multi-scale inception features.
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- Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation264
This work proposes a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs.
- Knowledge Adaptation for Efficient Semantic Segmentation253
This work proposes a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride and optimize the feature similarity in a transferred latent domain formulated by utilizing a pre-trained autoencoder.
- An End-to-End TextSpotter with Explicit Alignment and Attention244
A novel text-alignment layer is proposed that allows it to precisely compute convolutional features of a text instance in arbitrary orientation, which is the key to boost the performance of the model on the ICDAR 2015.
- ARCH++: Animation-Ready Clothed Human Reconstruction Revisited243
This paper introduces an end-to-end point based geometry encoder to better describe the semantics of the underlying 3D human body, in replacement of previous hand-crafted features, and proposes a co-supervising framework with cross-space consistency to jointly estimate the occupancy in both the posed and canonical spaces.
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- ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting189
This work adaptively fit arbitrarily-shaped text by a parameterized Bezier curve, which, compared with segmentation-based methods, can not only provide structured output but also controllable representation, which can be valuable for real-time applications.
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- Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors137
The method to infer 3D pose and shape of vehicles from a single image is presented, using a morphable wireframe model to generate a fine-scaled representation of vehicle shape and pose and jointly model the 3D bounding box as a coarse representation which improves robustness.
- DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution133
A dynamic, proposal-free, data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances, which achieves promising results on both ScanetNetV2 and S3DIS, and improves inference speed by more than 25% over the current state-of-the-art.
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- HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding63
A high-order CRFs based relation module (Con-HCRFs) is deployed to explore both point-wise smoothness and region-wise rigidity, and a position-aware flow estimation module is introduced to empower the CRFs to have a discriminative unary term.
Publication data from OpenAlex; 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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