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Works502 from public data
- Residual Dense Network for Image Super-Resolution4,163
This paper proposes residual dense block (RDB) to extract abundant local features via dense connected convolutional layers and uses global feature fusion in RDB to jointly and adaptively learn global hierarchical features in a holistic way.
- Large Scale Incremental Learning1,588
This work found that the last fully connected layer has a strong bias towards the new classes, and this bias can be corrected by a linear model, and with two bias parameters, this method performs remarkably well on two large datasets.
- Residual Dense Network for Image Restoration895
This work proposes residual dense block (RDB) to extract abundant local features via densely connected convolutional layers and proposes local feature fusion in RDB to adaptively learn more effective features from preceding and current local features and stabilize the training of wider network.
- Human Action Recognition and Prediction: A Survey838
This paper surveys the complete state-of-the-art techniques in action recognition and prediction, including existing models, popular algorithms, technical difficulties, popular action databases, evaluation protocols, and promising future directions.
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- Rethinking Classification and Localization for Object Detection712
A Double-Head method is proposed, which has a fully connected head focusing on classification and a convolution head for bounding box regression, and it is found that fc-head has more spatial sensitivity than conv-head.
- TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution655
Experimental results demonstrate that theTDAN is capable of alleviating occlusions and artifacts for temporal alignment and the TDAN-based VSR model outperforms several recent state-of-the-art VSR networks with a comparable or even much smaller model size.
- Visual Semantic Reasoning for Image-Text Matching625
A simple and interpretable reasoning model to generate visual representation that captures key objects and semantic concepts of a scene that outperforms the current best method for image retrieval and caption retrieval on MS-COCO and Flickr30K datasets.
- Rewrite the Stars619
This study attempts to reveal the star operation's ability of mapping inputs into high-dimensional, non-linear feature spaces-akin to kernel tricks-without widening the network.
- Tell Me Where to Look: Guided Attention Inference Network574
This work makes attention maps an explicit and natural component of the end-to-end training for the first time and provides self-guidance directly on these maps by exploring supervision from the network itself to improve them, and seamlessly bridge the gap between using weak and extra supervision if available.
- Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection552
The effect of the contamination of facial muscle activities on EEG signals is analyzed and it is found that most of the emotionally valuable content in EEG features are as a result of this contamination, however, the statistical analysis showed that EEG signals still carry complementary information in presence of facial expressions.
- Multi-View Clustering via Deep Matrix Factorization531
A deep matrix factorization framework for MVC is presented, where semi-nonnegative matrix factors are adopted to learn the hierarchical semantics of multi-view data in a layer-wise fashion and enforce the non-negative representation of each view in the final layer to be the same.
- Deep Collaborative Filtering via Marginalized Denoising Auto-encoder490
A general deep architecture for CF is proposed by integrating matrix factorization with deep feature learning, which leads to a parsimonious fit over the latent features as indicated by its improved performance in comparison to prior state-of-art models over four large datasets for the tasks of movie/book recommendation and response prediction.
- Towards the Semantic Web: Collaborative Tag Suggestions445
This work defines a set of general criteria for a good tagging system and proposes a collaborative tag suggestion algorithm using these criteria to spot high-quality tags and employs a goodness measure for tags derived from collective user authorities to combat spam.
- 442
- LatticeNet: Towards Lightweight Image Super-Resolution with Lattice Block367
A lightweight SR model, LatticeNet, is proposed, which uses series connection of LBs and the backward feature fusion, which can achieve superior accuracy on four available benchmark datasets against other state-of-the-art methods, while maintaining relatively low computation and memory requirements.
- Adversarial Feature Hallucination Networks for Few-Shot Learning324
Adversarial Feature Hallucination Networks (AFHN) is proposed which is based on conditional Wasserstein Generative Adversarial networks (cWGAN) and hallucinates diverse and discriminative features conditioned on the few labeled samples and comparative results substantiate the superiority of AFHN to existing data augmentation based FSL approaches and other state-of-the-art ones.
- SHARP: an architecture for secure resource peering321
The results demonstrate the power and practicality of the architecture, the effectiveness of oversubscription for protecting resource availability in the presence of failures, and the use with a decentralized barter economy for global PlanetLab resources.
- Generalized Transfer Subspace Learning Through Low-Rank Constraint305
Extensive experiments on synthetic data, and important computer vision problems such as face recognition application and visual domain adaptation for object recognition demonstrate the superiority of the proposed approach over the existing, well-established methods.
- 303
- Incomplete multi-modal visual data grouping296
An unsupervised method is proposed which well handles the incomplete multimodal data by transforming the original and incomplete data to a new and complete representation in a latent space, using a novel graph Laplacian term with a good probabilistic interpretation.
- Skeleton Aware Multi-modal Sign Language Recognition289
A novel skeleton Aware Multimodal SLR framework (SAM-SLR) is proposed and a Sign Language Graph Convolution Network (SL-GCN) to model the embedded dynamics and a novel Separable SpatialTemporal Convolution network (SSTCN) to exploit skeleton features to improve the recognition rate.
- 273
- UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild254
This work introduces UniControl, a new generative foundation model that consolidates a wide array of controllable condition-to-image (C2I) tasks within a singular framework, while still allowing for arbitrary language prompts, and enables pixel-level-precise image generation.
- 250
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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