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Works232 from public data
- RMPE: Regional Multi-person Pose Estimation1,884
This paper proposes a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes and can achieve 76:7 mAP on the MPII (multi person) dataset.
- Segment Anything in High Quality714
A learnable High-Quality Output Token is injected into SAM's mask decoder and is responsible for predicting the high-quality mask, which reuses and preserves the pre-trained model weights of SAM, while only introducing minimal additional parameters and computation.
- 669
- Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector648
A novel few-shot object detection network that aims at detecting objects of unseen categories with only a few annotated examples, which exploits the similarity between the few shot support set and query set to detect novel objects while suppressing false detection in the background.
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- Deep Saliency with Encoded Low Level Distance Map and High Level Features487
It is demonstrated that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features.
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- FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation339
This paper builds a few-shot segmentation dataset, FSS-1000, which consists of 1000 object classes with pixelwise annotation of ground-truth segmentation and finds that training the model from scratch using F SS-1000 achieves comparable and even better results than training with weights pre-trained by ImageNet.
- 334
- Deep High Dynamic Range Imaging with Large Foreground Motions311
This paper proposes the first non-flow-based deep framework for high dynamic range (HDR) imaging of dynamic scenes with large-scale foreground motions, and produces excellent results where color artifacts and geometric distortions are significantly reduced compared to existing state-of-the-art methods.
- Accurate Single Stage Detector Using Recurrent Rolling Convolution297
A novel single stage end-to-end trainable object detection network is proposed by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are deep in context.
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- CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement276
This paper presents quantitative and qualitative studies on different datasets to show that CascadePSP can reveal pixel-accurate segmentation boundaries using the novel refinement module without any finetuning.
- Semantic colorization with internet images271
A colorization system that leverages the rich image content on the internet and the user needs only to provide a semantic text label and segmentation cues for major foreground objects in the scene to achieve the desired result.
- Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion253
The MiVOS framework is presented, which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance, and a large-scale synthetic VOS dataset with pixel-accurate segmentation of 4.8M frames is contributed to facilitate future research.
- Memory-Attended Recurrent Network for Video Captioning240
The Memory-Attended Recurrent Network (MARN) for video captioning is proposed, in which a memory structure is designed to explore the full-spectrum correspondence between a word and its various similar visual contexts across videos in training data.
- Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers239
The explicit modeling of occlusion relationship with bilayer structure naturally decouples the boundaries of both the occluding and occluded instances, and considers the interaction between them during mask regression, to validate the efficacy of bilayer decoupling on both one-stage and two-stage object detectors with different backbones and network layer choices.
- Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications239
Instead of minimizing the nuclear norm, this paper proposes to minimize the partial sum of singular values, which implicitly encourages the target rank constraint, and shows that its results outperform those obtained by the conventional nuclear norm rank minimization method.
- Cross-Domain Adaptation for Animal Pose Estimation227
This paper proposed a novel cross-domain adaptation method to transform the animal pose knowledge from labeled animal classes to unlabeled animal classes, and uses the modest animal pose dataset to adapt learned knowledge to multiple animals species.
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- Attribute-Guided Face Generation Using Conditional CycleGAN224
This work condition the CycleGAN and proposes conditional CycleGAN, which is designed to handle unpaired training data because the training low/high-res and high-res attribute images may not necessarily align with each other, and to allow easy control of the appearance of the generated face via the input attributes.
- Learning Dual Convolutional Neural Networks for Low-Level Vision221
The proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner, and can generate the target signals according to the formation model for each particular application.
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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