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Works29 from public data
- Overview of two-stage object detection algorithms225
This paper reviews two-stage object detection algorithms used at this stage, explaining in detail the working principles of Faster R- CNN, R-FCN, FPN, and Casecade R-CNN and analyzing the similarities and differences between these four two- stages.
- MoLe-VLA: Dynamic Layer-skipping Vision Language Action Model via Mixture-of-Layers for Efficient Robot Manipulation89
A Spatial-Temporal Aware Router is introduced for MoLe to selectively activate only parts of the layers based on the robot’s current state, mimicking the brain's distinct signal pathways specialized for cognition and causal reasoning.
- NTIRE 2022 Challenge on Perceptual Image Quality Assessment84
This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2022 to address the emerging challenge of IQA by perceptual image processing algorithms.
- Multi-Level Personalized Federated Learning on Heterogeneous and Long-Tailed Data48
Extensive experiments on diverse real-world datasets for image classification and semantic segmentation validate that MuPFL consistently outperforms state-of-the-art baselines, even under extreme non-i.i.d. and long-tail conditions, marking significant advancements in both efficiency and effectiveness.
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- BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks37
This paper proposes a novel method named BEV Slice Attention Network (BEV-SAN), which first sample along the height dimension to build the global and local BEV slices, and proposes a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDar to determine the heights of local slices.
- Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-World30
A new learning paradigm of Cloud-Device Collaborative Continual Adaptation, which encourages collaboration between cloud and device and improves the generalization of the device model is proposed, which outperforms previous state-of-the-art test time adaptation and device-cloud collaboration methods.
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- MMTrail: A Multimodal Trailer Video Dataset with Language and Music Descriptions18
This work presents MMTrail, a large-scale multi-modality video-language dataset incorporating more than 20M trailer clips with visual captions, and 2M high-quality clips with multimodal captions incorporating various modality annotations, and proposes a systemic captioning framework.
- Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation13
Drawing inspiration from the the encoding characteristics of neuron activation in neural networks, the Mixture-of-Activation-Sparsity-Experts (MoASE) is proposed for the CTTA task and achieves state-of-the-art performance in both classification and segmentation tasks.
- Image Quality Assessment with Gradient Siamese Network13
This work introduces Gradient Siamese Network (GSN), skilled in capturing the gradient features between distorted images and reference images in full-reference image quality assessment (IQA) task, and innovatively design a multi-level fusion method to improve the efficiency of feature utilization.
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- Efficient Deweather Mixture-of-Experts with Uncertainty-aware Feature-wise Linear Modulation12
This work proposes an efficient MoE architecture with weight sharing across the experts, and proposes an Uncertainty-aware Router (UaR) to assign task-specific features to different FM modules with well-calibrated weights that enables MoFME to effectively learn diverse expert functions for multiple tasks.
- BEVUDA++: Geometric-Aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection9
An innovative geometric-aware teacher-student framework, BEVUDA++, to diminish the issue of domain shift, comprising a Reliable Depth Teacher (RDT) and a Geometric Consistent Student (GCS) model, is introduced.
- BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection8
A Multi-space Alignment Teacher-Student (MATS) framework to ease the domain shift accumulation, which consists of a Depth-Aware Teacher (DAT) and a Geometric-space Aligned Student (GAS) model.
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- RepCaM: Re-parameterization Content-aware Modulation for Neural Video Delivery7
This paper introduces a novel Re-parameterization Content-aware Modulation (RepCaM) method to modulate all the video chunks with an end-to-end training strategy, achieving state-of-the-art results in video restoration quality and delivery bandwidth compression.
- A Spatial Information Extraction Method Based on Multi-Modal Social Media Data: A Case Study on Urban Inundation6
The ability of the MIST-SMMD (Method of Identifying Spatiotemporal Information of Social Media Multimodal Data) to extract spatiotem temporal information from urban events at both coarse and fine levels is demonstrated and significant advantages of multimodal data in enhancing the precision of spatial information extraction are confirmed.
- VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness5
VeCAF optimizes a parametric data selection model by incorporating the training objective of the model being tuned, and guides the PVM towards the performance goal with improved data and computational efficiency.
- FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models2
This paper explores the FFN computation paradigm in LLMs and introduces FactorLLM, a novel approach that decomposes well-trained dense FFNs into sparse sub-networks without requiring any further modifications, while maintaining the same level of performance.
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- Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing2
The EulerNet is proposed, a new temporal feature fusion network in which the differential filter and residual pyramid are used to extract and amplify abnormal clues from continuous frames, respectively.
- RepCaM++: Exploring Transparent Visual Prompt With Inference-Time Re-Parameterization for Neural Video Delivery1
This paper introduces RepCaM++, an innovative framework based on a novel Re-parameterization Content-aware Modulation (RepCaM) module that uniformly modulates video chunks that achieves state-of-the-art results in video restoration quality and delivery bandwidth compression.
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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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