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- Dual Attention Network for Scene Segmentation6,830
New state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset is achieved without using coarse data.
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- Unsupervised Feature Selection Using Nonnegative Spectral Analysis545
A new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), which exploits the discriminative information and feature correlation simultaneously to select a better feature subset.
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- CoupleNet: Coupling Global Structure with Local Parts for Object Detection304
This paper proposes a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection and achieves state-of-the-art results on all three challenging datasets.
- Hi, magic closet, tell me what to wear!290
This paper collects a large clothing What-to-Wear dataset, and thoroughly annotates the whole dataset with 7 multi-value clothing attributes and 10 occasion categories via Amazon Mechanic Turk, to learn a generalize-well model and comprehensively evaluate it.
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- Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action-Gesture Recognition262
A novel decoupled spatial-temporal attention network (DSTA-Net) for skeleton-based action recognition that involves solely the attention blocks, allowing for modeling spatial-temporal dependencies between joints without the requirement of knowing their positions or mutual connections.
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- Normalized and Geometry-Aware Self-Attention Network for Image Captioning250
This paper proposes Normalized Self-Attention (NSA), a reparameterization of SA that brings the benefits of normalization inside SA and introduces a novel normalization method and demonstrates that it is both possible and beneficial to perform it on the hidden activations inside SA.
- Stacked Deconvolutional Network for Semantic Segmentation223
This work proposes a Stacked Deconvolutional Network (SDN) for semantic segmentation and achieves the new state-ofthe- art results on four datasets, including PASCAL VOC 2012, CamVid, GATECH, COCO Stuff.
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- Adaptive Context Network for Scene Parsing149
This paper proposes an Adaptive Context Network (ACNet) to capture the pixel-aware contexts by a competitive fusion of global context and local context according to different per-pixel demands.
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- Dual cross-media relevance model for image annotation143
A dual cross-media relevance model (DCMRM) is proposed for automatic image annotation, which estimates the joint probability by the expectation over words in a pre-defined lexicon.
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 2026-10-14. 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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