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Works180 from public data
- Relational Knowledge Distillation2,135
RKD allows students to outperform their teachers' performance, achieving the state of the arts on standard benchmark datasets and proposes distance-wise and angle-wise distillation losses that penalize structural differences in relations.
- Neural Video Compression with Diverse Contexts363
This paper introduces a group-based offset diversity where the cross-group interaction is proposed for better context mining and adopts a quadtree-based partition to increase spatial context diversity when encoding the latent representation in parallel.
- MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization287
This work proposes a novel IDE method that directly predicts the depth of the targeting 3D bounding box's center using sparse supervision, and demonstrates that MonoGRNet achieves state-of-the-art performance on challenging datasets.
- StableVideo: Text-driven Consistency-aware Diffusion Video Editing243
This paper develops a novel inter-frame propagation mechanism for diffusion video editing, which leverages the concept of layered representations to propagate the appearance information from one frame to the next.
- Virtualized Screen: A Third Element for Cloud–Mobile Convergence170
Two novel cloud-mobile applications, Cloud Browser and Cloud Phone, are presented to demonstrate the advantages of a virtualized screen, and technical challenges are discussed and addressed.
- WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet151
A deep learning network called WBC-Net, which is based on UNet++ and ResNet, is proposed to improve WBC image segmentation accuracy and achieves better WBC segmentation performance than several state-of-the-art methods.
- Rethinking Minimal Sufficient Representation in Contrastive Learning114
This work proposes to increase the mutual information between the representation and input as regularization to approximately introduce more task-relevant information, since the classic contrastive learning models have the risk of overfitting to the shared information between views.
- Affinity Derivation and Graph Merge for Instance Segmentation113
This work presents an instance segmentation scheme based on pixel affinity information, which is the relationship of two pixels belonging to the same instance, which uses two neural networks with similar structures to cluster pixels into instances.
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- Distributed multi-view video coding92
A practical framework of distributed multi-view video coding, in which inter-camera communication is avoided and the large computing complexity is moved from encoder to decoder, and the traditional inter frame is replaced by Wyner-Ziv frame.
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- Reliable Propagation-Correction Modulation for Video Object Segmentation76
This work aims to suppress error propagation through a correction mechanism with high reliability through a cascaded propagation-correction scheme and augments the reference cues by supplementing reliable feature patches to a maintained pool, thus offering more comprehensive and expressive object representations to the modulators.
- In Defense of the Classification Loss for Person Re-Identification75
This paper proposes a person re-id framework featured by channel grouping and multi-branch strategy, which divides global features into multiple channel groups and learns the discriminative channel group features by multi- Branches classification layers.
- Robust Referring Video Object Segmentation with Cyclic Structural Consensus74
This work jointly modeling the primary R-VOS problem and its dual (text reconstruction) and introduces a structural text-to-text cycle constraint to discriminate semantic consensus between video-text pairs and impose it in positive pairs, thereby achieving multi-modal alignment from both positive and negative pairs.
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- MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image49
This paper proposes an encoder-decoder network that generates such kind of multiple view-dependent point clouds from a single image by regressing their 3D coordinates and visibilities and introduces a novel geometric loss that is able to interpret discrepancy over 3D surfaces as opposed to 2D projective planes.
- MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoning48
This work proposes MMedAgent-RL, a reinforcement learning (RL)-based multi-agent framework that enables dynamic, optimized collaboration among medical agents, and introduces a curriculum learning (CL)-guided RL strategy with dynamic entropy regulation to address the inconsistency in specialist outputs.
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 2026-10-16. 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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