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Works132 from public data
- Unbiased Scene Graph Generation From Biased Training874
A novel SGG framework based on causal inference but not the conventional likelihood is presented, which uses Total Direct Effect (TDE) as the proposed final predicate score for unbiased SGG and can be widely applied in the community who seeks unbiased predictions.
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- NPA374
A neural news recommendation model with personalized attention (NPA) which exploits the embedding of user ID to generate the query vector for the word- and news-level attentions.
- Improving 3D Object Detection with Channel-wise Transformer316
This paper leverages the high-quality region proposal network and a Channel-wise Transformer architecture to constitute the two-stage 3D object detection framework (CT3D) with minimal hand-crafted design and achieves superior performance and excellent scalability.
- Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach313
This work proposes the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning across a federation of independent DaaS providers for the development of IoV applications, e.g., for traffic prediction and car park occupancy management.
- Visual Commonsense R-CNN299
A novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN), is presented to serve as an improved visual region encoder for high-level tasks such as captioning and VQA, and observes consistent performance boosts across them.
- A Survey on Deep Hashing Methods278
This survey detailedly investigates current deep hashing algorithms including deep supervised hashing and deep unsupervised hashing, and categorizes deep supervised hash methods into pairwise methods, ranking-based methods, pointwise methods as well as quantization according to how measuring the similarities of the learned hash codes.
- Camera-Aware Proxies for Unsupervised Person Re-Identification268
This paper proposes to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera, and designs both intra and inter-camera contrastive learning components for the Re-ID model to effectively learn the ID discrimination ability within and across cameras.
- Gradient Centralization: A New Optimization Technique for Deep Neural Networks228
It is shown that GC can regularize both the weight space and output feature space so that it can boost the generalization performance of DNNs, and improves the Lipschitzness of the loss function and its gradient so that the training process becomes more efficient and stable.
- Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization200
This paper introduces Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations by leveraging personal unique and cloth-independent gait information, and introduces this framework as GI-ReID, which enables an efficient and latency-free person identity matching for surveillance.
- Graph Contrastive Clustering175
This work first proposes a novel graph contrastive learning framework, and then applies it to the clustering task, resulting in the Graph Constrastive Clustering (GCC) method, which incorporates the latent category information to reduce the intra-clusters variance as well as increase the inter-cluster variance.
- Two Causal Principles for Improving Visual Dialog164
This paper unravels the design tricks adopted by the champion team MReaL-BDAI for Visual Dialog Challenge 2019: two causal principles for improving VisDial, which are model-agnostic, so they are applicable in any VisDial model.
- Elevation of blood glucose level predicts worse outcomes in hospitalized patients with COVID-19: a retrospective cohort study162
The findings may provide a simple and practical way to risk stratify COVID-19 inpatients for hierarchical management, particularly where medical resources are in severe shortage during the pandemic.
- Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles158
A joint auction-coalition formation framework to solve the allocation of UAV coalitions to groups of IoV components and it is shown that as the cooperation cost of the Uavs increases, the UAVs prefer to support the Iov components independently and not to form any coalition.
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- MaCAR: Urban Traffic Light Control via Active Multi-agent Communication and Action Rectification126
This work proposes a novel Multi-agent Communication and Action Rectification (MaCAR) framework which enables active communication between agents by considering the impact of synchronous actions of agents and outperforms state-of-the-art methods on both synthetic and real-world datasets.
- Online Convolutional Reparameterization101
This paper presents online convolutional re-parameterization (OREPA), a two-stage pipeline aiming to reduce the huge training overhead by squeezing the complex training-time block into a single convolution, and introduces a linear scaling layer for better optimizing the online blocks.
- Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation92
This paper introduces a Contourlet Decomposition Module (CDM) that decomposes low-level features with iterative laplacian pyramid and directional filter bank to mine the structural texture knowledge and proposes a Denoised Texture Intensity Equalization Module (DTIEM) to adaptively extract and enhance statistical texture knowledge through heuristics iterative quantization and denoised operation.
- DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation85
This paper proposes a new mask representation by applying the discrete cosine transform(DCT) to encode the high-resolution binary grid mask into a compact vector, termed DCT-Mask, which could be easily integrated into most pixel-based instance segmentation methods.
- Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition81
This work designs a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths.
- Dynamic Spatio-Temporal Graph-Based CNNs for Traffic Flow Prediction80
DST-GCNN is a two stream network that learns expressive features to represent spatio-temporal structures and predicts future traffic flows from surveillance video data and achieves competitive performances compared with the other state-of-the-art methods.
- Dense Interaction Learning for Video-based Person Re-identification77
A hybrid framework that takes the principal advantages of both CNN-based and Attention-based architectures to tackle video-based person re-ID difficulties and introduces Spatio-TEmporal Positional Embedding into the DI decoder to investigate the positional relation among the spatial-temporal inputs.
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- Traffic Flow Prediction with Vehicle Trajectories68
A spatiotemporal deep learning framework that mines the underlying causality of flows from historical vehicle trajectories and incorporates that into road traffic prediction and achieves over 5% error reduction when compared with the state-of-the-art approaches.
- Systemic Corticosteroids and Mortality in Severe and Critical COVID-19 Patients in Wuhan, China68
Absence of the beneficial effect in this study in contrast to that observed in the RECOVERY clinical trial may be due to biases in observational data, in particular prescription by indication bias.
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 2026-10-12. 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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