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Found 14 papers, 2 papers with code
Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking
In order to pass better ads from the early to the final stage ranking, we propose a multi-task learning framework for early stage ranking to capture multiple final stage ranking components (i. e. ads clicks and ads quality events) and their task relations.
An AFDM-Based Integrated Sensing and Communications
This paper considers an affine frequency division multiplexing (AFDM)-based integrated sensing and communications (ISAC) system, where the AFDM waveform is used to simultaneously carry communications information and sense targets.
Optimization of Forcemyography Sensor Placement for Arm Movement Recognition
To study the effectiveness of our optimization algorithm, a dataset for mechanical maintenance tasks using FMG armbands with 16 sensors is collected.
Optimal Streaming Algorithms for Graph Matching
For the dynamic streaming model, we present a one-pass algorithm that, with high probability, computes a maximum-weight $k$-matching of a weighted graph in $\tilde{O}(Wk^2)$ space and that has $\tilde{O}(1)$ update time, where $W$ is the number of distinct edge weights and the notation $\tilde{O}()$ hides a poly-logarithmic factor in the input size.
Graph Matching
Data Structures and Algorithms
Computational Complexity
Near-Optimal Algorithms for Point-Line Covering Problems
Both algorithms improve the running time of existing kernelization algorithms for Line Cover.
Computational Geometry Data Structures and Algorithms
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects.
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
Experimental results on DAVIS and FBMS show that the proposed method outperforms state-of-the-art unsupervised segmentation methods on various benchmark datasets.
SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting
In this paper, we focus on image inpainting task, aiming at recovering the missing area of an incomplete image given the context information.
Instance Embedding Transfer to Unsupervised Video Object Segmentation
We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks.
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection
When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e. g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects.
Contextual-based Image Inpainting: Infer, Match, and Translate
We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents.
A Taught-Obesrve-Ask (TOA) Method for Object Detection with Critical Supervision
Being inspired by child's learning experience - taught first and followed by observation and questioning, we investigate a critically supervised learning methodology for object detection in this work.
Semantic Segmentation with Reverse Attention
Recent development in fully convolutional neural network enables efficient end-to-end learning of semantic segmentation.
Ranked #94 on
Semantic Segmentation
on NYU Depth v2
Object Boundary Guided Semantic Segmentation
Semantic segmentation is critical to image content understanding and object localization.
