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Found 18 papers, 5 papers with code
MCS: An In-battle Commentary System for MOBA Games
This paper introduces a generative system for in-battle real-time commentary in mobile MOBA games.
IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus
Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE).
RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion Recognition
This module can construct the interaction between different modalities and capture long-range contextual information based on similarity clusters.
PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow
This paper proposes a novel paradigm for facial privacy protection that unifies multiple characteristics including anonymity, diversity, reversibility and security within a single lightweight framework.
MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection
Advanced manipulation techniques have provided criminals with opportunities to make social panic or gain illicit profits through the generation of deceptive media, such as forged face images.
A Concept Knowledge Graph for User Next Intent Prediction at Alipay
This paper illustrates the technologies of user next intent prediction with a concept knowledge graph.
Generative Graph Neural Networks for Link Prediction
Instead of sampling positive and negative links and heuristically computing the features of their enclosing subgraphs, GraphLP utilizes the feature learning ability of deep-learning models to automatically extract the structural patterns of graphs for link prediction under the assumption that real-world graphs are not locally isolated.
An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation
This study proposes an efficient framework for the few-shot skeleton-based TAS, including a data augmentation method and an improved model.
Spatial Transformer Network with Transfer Learning for Small-scale Fine-grained Skeleton-based Tai Chi Action Recognition
Human action recognition is a quite hugely investigated area where most remarkable action recognition networks usually use large-scale coarse-grained action datasets of daily human actions as inputs to state the superiority of their networks.
Dialogue Inspectional Summarization with Factual Inconsistency Awareness
Dialogue summarization has been extensively studied and applied, where the prior works mainly focused on exploring superior model structures to align the input dialogue and the output summary.
Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation
In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation.
Pushing the Envelope of Rotation Averaging for Visual SLAM
As an essential part of structure from motion (SfM) and Simultaneous Localization and Mapping (SLAM) systems, motion averaging has been extensively studied in the past years and continues to attract surging research attention.
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
The average video length of LaSOT is around 2, 500 frames, where each video contains various challenge factors that exist in real world video footage, such as the targets disappearing and re-appearing.
Projection Inpainting Using Partial Convolution for Metal Artifact Reduction
In this work, partial convolution is applied for projection inpainting, which only relies on valid pixels values.
SAU-Net: Efficient 3D Spine MRI Segmentation Using Inter-Slice Attention
Accurate segmentation of spine Magnetic Resonance Imaging (MRI) is highly demanded in morphological research, quantitative analysis, and diseases identification, such as spinal canal stenosis, disc herniation and degeneration.
TracKlinic: Diagnosis of Challenge Factors in Visual Tracking
For the analysis component, given the tracking results on all sequences, it investigates the behavior of the tracker under each individual factor and generates the report automatically.
Improving Human Annotation in Single Object Tracking
We then use different techniques to smooth the trajectories at certain degree.
Abstractive Dialog Summarization with Semantic Scaffolds
The demand for abstractive dialog summary is growing in real-world applications.
