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Found 12 papers, 5 papers with code
Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation
Experimental results on FineGrip demonstrate the feasibility of the panoptic perception task and the beneficial effect of multi-task joint optimization on individual tasks.
Structure Guided Large Language Model for SQL Generation
Existing models typically input queries and database schemas into the LLM and rely on the LLM to perform semantic-structure matching and generate structured SQL.
PETDet: Proposal Enhancement for Two-Stage Fine-Grained Object Detection
Fine-grained object detection (FGOD) extends object detection with the capability of fine-grained recognition.
Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services
Time series anomaly detection is crucial for industrial monitoring services that handle a large volume of data, aiming to ensure reliability and optimize system performance.
A Decoupled Multi-Task Network for Shadow Removal
Last, these features are converted to a target shadow-free image, affiliated shadow matte, and shadow image, supervised by multi-task joint loss functions.
Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding
While various vision-language pre-training objectives are studied in existing solutions, the document textline, as an intrinsic granularity in VDU, has seldom been explored so far.
Decoder Tuning: Efficient Language Understanding as Decoding
With the evergrowing sizes of pre-trained models (PTMs), it has been an emerging practice to only provide the inference APIs for users, namely model-as-a-service (MaaS) setting.
Efficient Bi-Level Optimization for Recommendation Denoising
To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time.
An Effective Iterated Two-stage Heuristic Algorithm for the Multiple Traveling Salesmen Problem
Each iteration of ITSHA consists of an initialization stage and an improvement stage.
Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources
Objectives: This paper develops two algorithms to achieve federated generalized linear mixed effect models (GLMM), and compares the developed model's outcomes with each other, as well as that from the standard R package (`lme4').
Local Label Point Correction for Edge Detection of Overlapping Cervical Cells
The qualitative and quantitative experimental results show that our LLPC can improve the quality of manual labels and the accuracy of overlapping cell edge detection.
Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors
Then, an unsupervised registration network is used to efficiently align the pre-procedural CT (pCT) with the inpainted iCT (inpCT) image.
