Search Results for author:
Found 18 papers, 4 papers with code
DDT: Dual-branch Deformable Transformer for Image Denoising
Transformer is beneficial for image denoising tasks since it can model long-range dependencies to overcome the limitations presented by inductive convolutional biases.
Adaptive Scenario Discovery for Crowd Counting
Crowd counting, i. e., estimation number of the pedestrian in crowd images, is emerging as an important research problem with the public security applications.
LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion
Notably, LoGoNet ranks 1st on Waymo 3D object detection leaderboard and obtains 81. 02 mAPH (L2) detection performance.
Fast Video Crowd Counting with a Temporal Aware Network
Crowd counting aims to count the number of instantaneous people in a crowded space, and many promising solutions have been proposed for single image crowd counting.
Scene Text Recognition with Temporal Convolutional Encoder
In this paper, we study text recognition framework by considering the long-term temporal dependencies in the encoder stage.
Document Layout Analysis via Dynamic Residual Feature Fusion
The document layout analysis (DLA) aims to split the document image into different interest regions and understand the role of each region, which has wide application such as optical character recognition (OCR) systems and document retrieval.
A Survey of Human-in-the-loop for Machine Learning
Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for computers in the pipeline with the help of machine-based approaches.
Document Image Layout Analysis via Explicit Edge Embedding Network
Layout analysis from a document image plays an important role in document content understanding and information extraction systems.
Document Layout Analysis with Aesthetic-Guided Image Augmentation
To measure the proposed image layer modeling method, we propose a manually-labeled non-Manhattan layout fine-grained segmentation dataset named FPD.
Cross-Domain Document Layout Analysis via Unsupervised Document Style Guide
Our framework is an unsupervised document layout analysis framework.
Multi-channel Attentive Graph Convolutional Network With Sentiment Fusion For Multimodal Sentiment Analysis
This paper proposes a Multi-channel Attentive Graph Convolutional Network (MAGCN), consisting of two main components: cross-modality interactive learning and sentimental feature fusion.
Progressive Scene Text Erasing with Self-Supervision
Scene text erasing seeks to erase text contents from scene images and current state-of-the-art text erasing models are trained on large-scale synthetic data.
Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection
To address these problems, we construct the homogeneous structure between the point cloud and images to avoid projective information loss by transforming the camera features into the LiDAR 3D space.
FairMonitor: A Four-Stage Automatic Framework for Detecting Stereotypes and Biases in Large Language Models
Detecting stereotypes and biases in Large Language Models (LLMs) can enhance fairness and reduce adverse impacts on individuals or groups when these LLMs are applied.
Progressive Evidence Refinement for Open-domain Multimodal Retrieval Question Answering
Firstly, utilizing compressed evidence features as input to the model results in the loss of fine-grained information within the evidence.
DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding
Our DCQA dataset is expected to foster research on understanding visualizations in documents, especially for scenarios that require complex reasoning for charts in the visually-rich document.
UMAAF: Unveiling Aesthetics via Multifarious Attributes of Images
Then, to better utilize image attributes in aesthetic assessment, we propose the Unified Multi-attribute Aesthetic Assessment Framework (UMAAF) to model both absolute and relative attributes of images.
