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Works465 from public data
- Deep High-Resolution Representation Learning for Human Pose Estimation5,644
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.
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- Object-Contextual Representations for Semantic Segmentation1,756
This paper addresses the semantic segmentation problem with a focus on the context aggregation strategy, and presents a simple yet effective approach, object-contextual representations, characterizing a pixel by exploiting the representation of the corresponding object class.
- Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision1,250
This paper proposes a novel consistency regularization approach, called cross pseudo supervision (CPS), which imposes the consistency on two segmentation networks perturbed with different initialization for the same input image.
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- Conditional DETR for Fast Training Convergence1,117
The approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention, which narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training.
- A Survey on Learning to Hash1,099
This paper presents a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise Similarity preserving, implicit similarity preserve, as well as quantization, and discusses their relations.
- MARS: A Video Benchmark for Large-Scale Person Re-Identification1,088
It is shown that CNN in classification mode can be trained from scratch using the consecutive bounding boxes of each identity, and the learned CNN embedding outperforms other competing methods considerably and has good generalization ability on other video re-id datasets upon fine-tuning.
- HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation952
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.
- Deeply-Learned Part-Aligned Representations for Person Re-identification813
This paper proposes a simple yet effective human part-aligned representation for handling the body part misalignment problem, and shows state-of-the-art results over standard datasets, Market-1501,CUHK03, CUHK01 and VIPeR.
- Structured Knowledge Distillation for Semantic Segmentation748
This paper starts from the straightforward scheme, pixel-wise distillation, which applies the distillation scheme originally introduced for image classification and performs knowledge distillation for each pixel separately, and proposes to distill the structured knowledge from cumbersome networks into compact networks, motivated by the fact that semantic segmentation is a structured prediction problem.
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- Part-Aligned Bilinear Representations for Person Re-identification557
A novel network that learns a part-aligned representation for person re-identification that handles the body part misalignment problem, that is, body parts are misaligned across human detections due to pose/viewpoint change and unreliable detection.
- DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection538
This paper proposes a multi-task deep saliency model based on a fully convolutional neural network with global input (whole raw images) and global output (Whole saliency maps) and presents a graph Laplacian regularized nonlinear regression model for saliency refinement.
- Context Autoencoder for Self-supervised Representation Learning522
The CAE design encourages the separation of learning the encoder (representation) from completing the pertaining tasks: masked representation prediction and masked patch reconstruction tasks, and making predictions in the encoded representation space empirically shows the benefit to representation learning.
- Lite-HRNet: A Lightweight High-Resolution Network506
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.
- Automatic salient object segmentation based on context and shape prior497
A novel automatic salient object segmentation algorithm which integrates both bottom-up salient stimuli and object-level shape prior, leading to binary segmentation of the salient object.
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- Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression398
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.
- Generation of cloned transgenic pigs rich in omega-3 fatty acids386
The generation of cloned pigs that express a humanized Caenorhabditis elegans gene, fat-1, encoding an n-3 fatty acid desaturase is described, and the hfat-1 transgenic pigs produce high levels of n- 3 fatty acids from n-6 analogs, and their tissues have a significantly reduced ratio ofn-6/n-4 fatty acids.
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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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