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- Rethinking Efficient Lane Detection via Curve Modeling223
This paper proposes to exploit the parametric Bézier curve due to its ease of computation, stability, and high freedom degrees of transformations, and proposes the deformable convolution-based feature flip fusion, for exploiting the symmetry properties of lanes in driving scenes.
- DMT: Dynamic mutual training for semi-supervised learning214
This paper proposes mutual training between two different models by a dynamically re-weighted loss function, called Dynamic Mutual Training (DMT), and quantifies inter-model disagreement by comparing predictions from two different models to dynamically re-weight loss in training.
- PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection212
A one-class prompt learning method for few-shot anomaly detection, termed PromptAD, which proposes semantic concatenation which can transpose normal prompts into anomaly prompts by concatenating normal prompts with anomaly suffixes, and introduces the concept of explicit anomaly margin.
- Re-ID Driven Localization Refinement for Person Search160
A differentiable ROI transform layer is developed to effectively transform the bounding boxes from the original images so that the box coordinates can be supervised by the re-ID training other than the original detection task.
- Efficient multimodal large language models: a survey157
This survey summarizes the timeline of representative efficient MLLMs, the current state of research in structures and strategies, and the applications, and the limitations of current efficient MLLM research and promising future directions are discussed.
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- Unveiling the Power of CLIP in Unsupervised Visible-Infrared Person Re-Identification85
A new prompt learning paradigm for unsupervised visible-infrared person re-identification (USL-VI-ReID) by taking full advantage of the visual-text representation ability from CLIP is proposed and a new memory-swapping contrastive learning is proposed.
- Executive Function Training for Children with Attention Deficit Hyperactivity Disorder83
Although replication with a larger sample and an active control group are needed, EF training program with multiple EF focus and parent involving in real-life activities could be a potentially promising intervention associated with significant EF (near transfer) and ADHD symptoms improvement (far transfer).
- An Adaptive Attention Fusion Mechanism Convolutional Network for Object Detection in Remote Sensing Images82
A convolutional network model with an adaptive attention fusion mechanism (AAFM) based on the backbone network of EfficientDet that improves accuracy and has stronger robustness of the optical image dataset DIOR.
- Semi-Supervised Semantic Segmentation via Dynamic Self-Training and Class-Balanced Curriculum74
The method, Dynamic Self-Training and Class-Balanced Curriculum (DST-CBC), exploits inter-model disagreement by prediction confidence to construct a dynamic loss robust against pseudo label noise, enabling it to extend pseudo labeling to a class-balanced curriculum learning process.
- Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds69
A Boundary Prediction Module (BPM) to predict boundary points and a boundary-aware Geometric Encoding Module (GEM) is designed to encode geometric information and aggregate features with discrimination in a neighborhood, so that the local features belonging to different categories will not be polluted by each other.
- Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation68
This paper proposes a hierarchical model to learn the global context for document-level neural machine translation (NMT), which feedback the extracted global document context to each word in a top-down fashion to distinguish different translations of a word according to its specific surrounding context.
- Frequency-aware Camouflaged Object Detection66
A frequency-aware context aggregation module to suppress high-frequency information and aggregate multi-scale features from a frequency perspective, an adaptive frequency attention module to enhance the features of the learned important frequency components, and a gradient-weighted loss function to guide the proposed method to pay more attention to contour details.
- Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoning60
This paper brings the first omni-scale supervision method to point cloud segmentation via the proposed gradual Receptive Field Component Reasoning (RFCR) and brings new state-of-the-art performances for S3DIS as well as Semantic3D and ranks the 1st in the ScanNet benchmark among all the point-based methods.
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- Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing51
This paper focuses on image segmentation, image correction and spatial-spectral dimensional denoising of images in hyperspectral image preprocessing to improve the classification accuracy of hyperspectRAL images.
- Molecular and phenotypic spectrum of Noonan syndrome in Chinese patients51
The genotypic and phenotypic spectra of NS in China are described, providing new insights into distinctive clinical features due to specific pathogenic variant, and ubiquitous abnormal intracranial structure due to SHOC2 pathogenic variants.
- FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds50
A novel framework called FVNet for 3D front-view proposal generation and object detection from point clouds, which achieves real-time performance with 12ms per point cloud sample and outperforms state-of-the-art techniques which take either camera images or point clouds as input, in terms of accuracy and inference time.
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- One-for-More: Continual Diffusion Model for Anomaly Detection41
An iterative singular value decomposition method based on the transitive property of linear representation, which consumes tiny memory and incurs almost no performance loss, and an anomaly-masked network to enhance the condition mechanism of the diffusion model are proposed.
- GEOcc: Geometrically Enhanced 3D Occupancy Network With Implicit-Explicit Depth Fusion and Contextual Self-Supervision41
GEOcc, a Geometric-Enhanced Occupancy network tailored for vision-only surround-view perception achieves State-of-the-Art performance on the Occ3D-nuScenes dataset with the least image resolution needed and the most weightless image backbone compared with current models, marking an improvement of 3.3% due to the proposed contributions.
- Boosting Night-Time Scene Parsing With Learnable Frequency39
This paper proposes to exploit the image frequency distributions for night-time scene parsing with a Learnable Frequency Encoder (LFE) to model the relationship between different frequency coefficients to measure all frequency components dynamically and proposes a Spatial Frequency Fusion module (SFF) that fuses both spatial and frequency information to guide the extraction of spatial context features.
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- Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning32
This paper designs a dual-branch network equipped with an active labeling strategy to maximize the power of tiny parts of labels and to directly realize 2D-to-3D knowledge transfer and establishes a cross-modal self-training framework, which iterates between parameter updating and pseudolabel estimation.
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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