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- Coordinate Attention for Efficient Mobile Network Design6,424
This paper proposes a novel attention mechanism for mobile networks by embedding positional information into channel attention, which it is called "coordinate attention", which factorizes channel attention into two 1D feature encoding processes that aggregate features along the two spatial directions.
- Deeply Supervised Salient Object Detection with Short Connections1,458
This paper proposes a new salient object detection method by introducing short connections to the skip-layer structures within the HED architecture, which takes full advantage of multi-level and multi-scale features extracted from FCNs, providing more advanced representations at each layer, a property that is critically needed to perform segment detection.
- Rotate to Attend: Convolutional Triplet Attention Module1,142
The method is simple as well as efficient and can be easily plugged into classic backbone networks as an add-on module and supports the intuition on the importance of capturing dependencies across dimensions when computing attention weights.
- A Simple Pooling-Based Design for Real-Time Salient Object Detection1,100
This work solves the problem of salient object detection by investigating how to expand the role of pooling in convolutional neural networks by building a global guidance module (GGM) and designing a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down path- way.
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- Large Selective Kernel Network for Remote Sensing Object Detection887
This paper considers these priors and proposes the lightweight Large Selective Kernel Network (LSKNet), which can dynamically adjust its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios.
- Strip Pooling: Rethinking Spatial Pooling for Scene Parsing817
Beyond conventional spatial pooling that usually has a regular shape of NxN, a new pooling strategy is introduced, called strip pooling, which considers a long but narrow kernel, i.e., 1xN or Nx1.
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- VOLO: Vision Outlooker for Visual Recognition422
A new simple and generic architecture, termed Vision Outlooker (VOLO), which implements a novel outlook attention operation that dynamically conduct the local feature aggregation mechanism in a sliding window manner across the input image, which can more efficiently encode fine-level features that are essential for high-performance visual recognition.
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- Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground346
This work identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter, and proposes a new high quality dataset and updates the previous saliency benchmark.
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- Conv2Former: A Simple Transformer-Style ConvNet for Visual Recognition290
A deep look at the internal structure of self-attention is taken and a simple Transformer style convolutional neural network (ConvNet) is presented for visual recognition and a family of hierarchical ConvNets using the proposed convolutional modulation, termed Conv2Former is built.
- Rethinking Bottleneck Structure for Efficient Mobile Network Design275
This paper proposes to flip the structure and present a novel bottleneck design, called the sandglass block, that performs identity mapping and spatial transformation at higher dimensions and thus alleviates information loss and gradient confusion effectively and adds it into the search space of neural architecture search method DARTS.
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- 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.
- AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation229
This work presents All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation based on two essential designs that derive multiple groups of fine-grained flow fields from one pair of updated coarse flows for performing backward warping on the input frames separately.
- Localization Distillation for Dense Object Detection208
This paper reformulates the knowledge distillation process on localization to present a novel localization distillation method which can efficiently transfer the localization knowledge from the teacher to the student and heuristically introduces the concept of valuable localization region that can aid to selectively distill the semantic and localization knowledge for a certain region.
- CamoFormer: Masked Separable Attention for Camouflaged Object Detection198
Inspired by the multi-head self-attention in Transformers, a simple masked separable attention (MSA) for camouflaged object detection is presented and proposed to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results.
- L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation192
L2G, a simple online local-to-global knowledge transfer framework for high-quality object attention mining, observes that classification models can discover object regions with more details when replacing the input image with its local patches, and utilizes a global network to learn complementary attention knowledge across multiple local attention maps online.
- 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.
- Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge, and Skeleton160
Though these tasks are naturally quite different, the proposed network can work well on all of them and even perform better than current single-purpose state-of-the-art methods.
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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