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- Xiuqing WuSuggested from co-authorship
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Works588 from public data
- NUS-WIDE3,409
The benchmark results indicate that it is possible to learn effective models from sufficiently large image dataset to facilitate general image retrieval and four research issues on web image annotation and retrieval are identified.
- Richer Convolutional Features for Edge Detection1,240
RCF encapsulates all convolutional features into more discriminative representation, which makes good usage of rich feature hierarchies, and is amenable to training via backpropagation, and achieves state-of-the-art performance on several available datasets.
- Correlative multi-label video annotation504
A third paradigm is proposed which simultaneously classifies concepts and models correlations between them in a single step by using a novel Correlative Multi-Label (CML) framework and is compared with the state-of-the-art approaches in the first and second paradigms on the widely used TRECVID data set.
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- Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection450
This paper explores a completely novel and different perspective to perform LQE – based on the learned distributions of the four parameters of the bounding box – and develops a considerably lightweight Distribution-Guided Quality Predictor (DGQP) for reliable LqE based on GFLV1, thus producing GFLv2.
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- Outer Product-based Neural Collaborative Filtering363
Extensive experiments demonstrate the effectiveness of the proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model.
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- Centralized Feature Pyramid for Object Detection342
A Centralized Feature Pyramid (CFP) network for object detection, which is based on a globally explicit centralized feature regulation, which not only has the ability to capture the global long-range dependencies but also efficiently obtain an all-round yet discriminative feature representation.
- CLIP-Driven Fine-Grained Text-Image Person Re-Identification329
A CLIP-driven Fine-grained information excavation framework (CFine) to fully utilize the powerful knowledge of CLIP for TIReID and proposes a multi-level global feature learning (MGF) module that fully mines the discriminative local information within each modality, thereby emphasizing identity-related discrim inative clues through enhanced interaction between global image (text) and informative local patches (words).
- Feature Pyramid Transformer317
This work proposes a fully active feature interaction across both space and scales, called Feature Pyramid Transformer (FPT), which transforms any feature pyramid into another feature pyramid of the same size but with richer contexts, by using three specially designed transformers in self-level, top-down, and bottom-up interaction fashion.
- Generalized Nonconvex Nonsmooth Low-Rank Minimization293
In theory, it is proved that IRNN decreases the objective function value monotonically, and any limit point is a stationary point, which enhances the low-rank matrix recovery compared with state-of-the-art convex algorithms.
- Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution289
This work proposes a spatially-adaptive feature modulation (SAFM) mechanism for efficient SR design that is 3× smaller than state-of-the-art efficient SR methods, e.g., IMDN, and yields comparable performance with much less memory usage.
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- Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection271
- Image annotation by k NN-sparse graph-based label propagation over noisily tagged web images269
This article exploits the problem of annotating a large-scale image corpus by label propagation over noisily tagged web images by proposing a novel kNN-sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously.
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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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