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Works217 from public data
- 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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- Automatic Spatially-Aware Fashion Concept Discovery341
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites, and decomposes the visual-semantic embedding space into multiple concept-specific subspaces to facilitate structured browsing and attribute-feedback product retrieval.
- Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers232
This paper introduces the use of object identifiers and object-centric representations to interact with scenes at the object level, and decomposes the input 3D scene into a set of object proposals, each assigned a unique identifier token, which enables efficient object referencing and grounding during user-assistant interactions.
- Completed robust local binary pattern for texture classification171
A robust framework of LBP is proposed, named Completed Robust Local Binary Pattern (CRLBP), in which the value of each center pixel in a 3x3 local area is replaced by its average local gray level, which is more robust to noise and illumination variants.
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- FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification124
This work analyzes vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults.
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- Robust Estimators for Variance-Based Device-Free Localization and Tracking110
A new estimator, least squares variance-based radio tomography (LSVRT), is proposed, which reduces the impact of the variations caused by intrinsic motion and which achieves better localization accuracy and does not require manually tuning additional parameters compared to VRTI.
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- No Reference Quality Assessment for Screen Content Images Using Stacked Autoencoders in Pictorial and Textual Regions78
An effective and novel blind quality evaluation metric for SCIs by using stacked autoencoders (SAE) based on pictorial and textual regions based on a weighted model is presented.
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- MaskCOV: A random mask covariance network for ultra-fine-grained visual categorization64
Experimental results of the proposed novel random mask covariance network (MaskCOV), which integrates an auxiliary self-supervised learning module with a powerful in-image data augmentation scheme for the ultra-FGVC, demonstrate its superiority and potential of MaskCOV concept, which pushes research boundary forward from the fine-grained to theUltra-fine-Grained visual categorization.
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- MobileFAN: Transferring deep hidden representation for face alignment49
An effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder is proposed, which achieves superior or equivalent performance compared with state-of-the-art models.
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- Patchy Image Structure Classification Using Multi-Orientation Region Transform44
Experimental results demonstrate the effectiveness and superiority of the proposed MORT over the state-of-the-art methods in classifying patchy image structures and can be extended to combine with the deep convolutional neural network techniques, for further enhancement of classification accuracy.
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- DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models41
This paper proposes a two-step approach DreamInpainter, which employs a discriminative token selection module to eliminate redundant subject details, preserving the subject's identity while allowing changes according to other conditions such as mask shape and text prompts.
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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 2026-10-15. 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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