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- Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions5,324
The Pyramid Vision Transformer (PVT) is introduced, which overcomes the difficulties of porting Transformer to various dense prediction tasks and is validated through extensive experiments, showing that it boosts the performance of many downstream tasks, including object detection, instance and semantic segmentation.
- PVT v2: Improved baselines with pyramid vision transformer2,673
This work improves the original Pyramid Vision Transformer (PVT v1) by adding three designs: a linear complexity attention layer, an overlapping patch embedding, and a convolutional feed-forward network to reduce the computational complexity of PVT v1 to linearity and provide significant improvements on fundamental vision tasks.
- Shape Robust Text Detection With Progressive Scale Expansion Network860
A novel Progressive Scale Expansion Network (PSENet) is proposed, which can precisely detect text instances with arbitrary shapes and is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances.
- Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal537
This paper presents a multi-task perspective, which is not embraced by any existing work, to jointly learn both detection and removal in an end-to-end fashion that aims at enjoying the mutually improved benefits from each other.
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- Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection271
- YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-Time Object Detection246
The core design is based on a series of investigations on how multi-branch features of the basic block and convolutions with different kernel sizes affect the detection performance of objects at different scales, and the outcome is a new strategy that can significantly enhance multi-scale feature representations of real-time object detectors.
- LSKNet: A Foundation Lightweight Backbone for Remote Sensing237
This paper considers the significance of the identified priors and proposes a lightweight Large Selective Kernel Network (LSKNet) backbone, which can dynamically adjust its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios.
- Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation237
A novel joint Task-Recursive Learning (TRL) framework for the closing-loop semantic segmentation and monocular depth estimation tasks and propagate previous learning experiences on both tasks into the next network evolution by explicitly concatenating previous responses.
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- RigNet: Repetitive Image Guided Network for Depth Completion180
A repetitive design is explored in the authors' image guided network to gradually and sufficiently recover depth values and introduces a repetitive guidance module based on dynamic convolution, in which an efficient convolution factorization is proposed to simultaneously reduce its complexity and progressively model high-frequency structures.
- One-Stage Cascade Refinement Networks for Infrared Small Target Detection177
The one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposal for the low-level refinement head, allows OSCAR to process the same target in a cascade coarse-to-fine manner and demonstrates that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency.
- 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.
- Neuron-level Structured Pruning using Polarization Regularizer168
Theoretically, it is proved that polarization regularizer pushes some scaling factors to 0 and others to a value a > 0 and experimentally, it is shown that structured pruning using polarization regularizer achieves much better results than using L1 regular-izer.
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- CrossKD: Cross-Head Knowledge Distillation for Object Detection160
This paper presents a general and effective prediction mimicking distillation scheme, called CrossKD, which delivers the intermediate features of the student's detection head to the teacher's detection head, and the resulting crosshead predictions are forced to mimic the teacher's predictions.
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- Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-Guided Feature Imitation122
Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) are proposed for distilling one-stage detectors, respectively, which takes the rank of candidate boxes from teachers as a new form of knowledge to distill, which consistently outperforms the traditional soft label distillation.
- Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark120
A novel framework with several improvements that introduces Priors-Based Regularization to learn distribution knowledge from unpaired depth maps and prevent model from being incorrectly trained and presents Statistics-Based Mask strategy to tune the number of removed pixels within textureless regions, using dynamic statistics.
- Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection95
Strip R-CNN is the first work to systematically explore large strip convolutions for remote sensing object detection, finding that strip convolutions enable directional feature aggregation along the dominant spatial dimension of slender objects, reducing background interference while preserving essential geometric information.
- DLGSANet: Lightweight Dynamic Local and Global Self-Attention Network for Image Super-Resolution83
A sparse global self-attention (SparseGSA) module is developed to select the most useful similarity values so that the most useful global features can be better utilized for image reconstruction.
- Pick of the Bunch: Detecting Infrared Small Targets Beyond Hit-Miss Trade-Offs via Selective Rank-Aware Attention76
This work proposes SeRankDet, a deep network that achieves high accuracy beyond the conventional hit-miss trade-off, by following the “Pick of the Bunch” principle, and sets new benchmarks in state-of-the-art performance across multiple public datasets.
- Densely Connected Bidirectional LSTM with Applications to Sentence Classification68
A novel multi-layer RNN model called densely connected bidirectional long short-term memory (DC-Bi-LSTM) is proposed, which essentially represents each layer by the concatenation of its hidden state and all preceding layers' hidden states, followed by recursively passing each layer's representation to all subsequent layers.
- ADNet: Lane Shape Prediction via Anchor Decomposition66
This paper revisits the limitations of anchor-based lane detection methods and introduces the Large Kernel Attention for Feature Pyramid Network (FPN) and the General Lane IoU (GLIoU) loss, which significantly improves the performance of ADNet in complex scenarios.
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.
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