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Found 15 papers, 8 papers with code
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation
Thus, it is easy to use a pre-trained GoogLeNet for large-scale image classification problem and fine tune our connected layers on an large scale database of aesthetic related images: AVA, i. e. \emph{domain adaptation}.
Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks
In addition, it is crucial to introduce lexical knowledge to detect the toxicity of posts, which has been a challenge for researchers.
Beyond Co-occurrence: Multi-modal Session-based Recommendation
(2) How to fuse these heterogeneous descriptive information to comprehensively infer user interests?
TBFormer: Two-Branch Transformer for Image Forgery Localization
In this paper, we propose a Transformer-style network with two feature extraction branches for image forgery localization, and it is named as Two-Branch Transformer (TBFormer).
Aesthetic Attributes Assessment of Images
This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute.
Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation
Secondly, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling.
Facial Makeup Transfer Combining Illumination Transfer
To meet the women appearance needs, we present a novel virtual experience approach of facial makeup transfer, developed into windows platform application software.
Pseudo-labelling and Meta Reweighting Learning for Image Aesthetic Quality Assessment
In the tasks of image aesthetic quality evaluation, it is difficult to reach both the high score area and low score area due to the normal distribution of aesthetic datasets.
RealMedDial: A Real Telemedical Dialogue Dataset Collected from Online Chinese Short-Video Clips
We collected and annotated a wide range of meta-data with respect to medical dialogue including doctor profiles, hospital departments, diseases and symptoms for fine-grained analysis on language usage pattern and clinical diagnosis.
Hate Speech Detection via Dual Contrastive Learning
Our framework jointly optimizes the self-supervised and the supervised contrastive learning loss for capturing span-level information beyond the token-level emotional semantics used in existing models, particularly detecting speech containing abusive and insulting words.
KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions
Secondly, social media datasets suffer from the challenges of low annotated data.
Side Information-Driven Session-based Recommendation: A Survey
Emerging efforts incorporate various kinds of side information into their methods for enhancing task performance.
FineRec:Exploring Fine-grained Sequential Recommendation
Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors.
Disentangling ID and Modality Effects for Session-based Recommendation
At the item level, we introduce a co-occurrence representation schema to explicitly incorporate cooccurrence patterns into ID representations.
