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- Deep High-Resolution Representation Learning for Human Pose Estimation5,649
This paper proposes a network that maintains high-resolution representations through the whole process of human pose estimation and empirically demonstrates the effectiveness of the network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.
- Deep High-Resolution Representation Learning for Visual Recognition5,145
The superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, is shown, suggesting that the HRNet is a stronger backbone for computer vision problems.
- CvT: Introducing Convolutions to Vision Transformers2,569
A new architecture is presented that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both de-signs, and the positional encoding, a crucial component in existing Vision Transformers, can be safely re-moved in this model.
- Simple Baselines for Human Pose Estimation and Tracking2,149
This work provides simple and effective baseline methods for pose estimation that are helpful for inspiring and evaluating new ideas for the field and achieved on challenging benchmarks.
- Dynamic Head: Unifying Object Detection Heads with Attentions1,044
This paper presents a novel dynamic head framework to unify object detection heads with attentions by coherently combining multiple self-attention mechanisms between feature levels for scale- awareness, among spatial locations for spatial-awareness, and within output channels for task-awareness that significantly improves the representation ability of object detection Heads without any computational overhead.
- Integral Human Pose Regression963
This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues and is differentiable, efficient, and compatible with any heat map based methods.
- HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation954
HigherHRNet is presented, a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids that surpasses all top-down methods on CrowdPose test and achieves new state-of-the-art result on COCO test-dev, suggesting its robustness in crowded scene.
- Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks657
Florence-2, a novel vision foundation model with a unified, prompt-based representation for various computer vision and vision-language tasks, is introduced and demonstrated to be a strong vision foundation model contender with un-precedented zero-shot and fine-tuning capabilities.
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- Lite-HRNet: A Lightweight High-Resolution Network507
An efficient high-resolution network, Lite-HRNet, is presented, which demonstrates superior results on human pose estimation over popular lightweight networks and can be easily applied to semantic segmentation task in the same lightweight manner.
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- Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding405
A comprehensive empirical study shows that the new ViT significantly outperforms several strong baselines, including the existing ViT models and their ResNet counterparts, and the Pyramid Vision Transformer from a concurrent work, on a range of vision tasks, including image classification, object detection, and segmentation.
- Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression401
It is empirically show that the proposed direct regression method outperforms keypoint detection and grouping methods and achieves superior bottom-up pose estimation results on two benchmark datasets, COCO and CrowdPose.
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- Unified Contrastive Learning in Image-Text-Label Space301
This work intro-duce a new formulation of Unified Con-trastive Learning (UniCL) with a single learning objective to seamlessly prompt the synergy of two data types, universally for image recognition in zero-shot, linear-probing, fully finetuning and transfer learning sce-narios.
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- MiniViT: Compressing Vision Transformers with Weight Multiplexing186
MiniViT, a new compression framework, which achieves parameter reduction in vision transformers while retaining the same performance, and makes the weights shared across layers, while imposing a transformation on the weights to increase diversity.
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- Mariana56
Mariana, the Tencent deep learning platform, which utilizes GPU and CPU cluster to train models parallelly with three frameworks, achieves state-of-the-art acceleration performance, and plays a key role in training models and improving quality for automatic speech recognition and image recognition in Tencent WeChat, a mobile social platform.
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- Bottom-up Higher-Resolution Networks for Multi-Person Pose Estimation41
Higher-Resolution Network (HigherHRNet) is proposed, which is a simple extension of the High-Res resolution Network (HRNet), which generates higher-resolution feature maps by deconvolving the high- resolution feature maps outputted by HRNet, which are spatially more accurate for small and medium persons.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. 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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