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Found 17 papers, 7 papers with code
Interactive Latent Knowledge Selection for E-Commerce Product Copywriting Generation
As the multi-modal e-commerce is thriving, high-quality advertising product copywriting has gain more attentions, which plays a crucial role in the e-commerce recommender, advertising and even search platforms. The advertising product copywriting is able to enhance the user experience by highlighting the product’s characteristics with textual descriptions and thus to improve the likelihood of user click and purchase.
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis
Specifically, on the client side, we condense the knowledge of each client into a small dataset and further enhance the condensation procedure with latent distribution constraints, facilitating the effective capture of high-quality knowledge.
Towards Federated Graph Learning in One-shot Communication
Specifically, for effective graph learning in one communication round, our method estimates and aggregates class-wise feature distribution statistics to construct a global pseudo-graph on the server, facilitating the training of a global graph model.
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis
In the evolving application of medical artificial intelligence, federated learning is notable for its ability to protect training data privacy.
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation
In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity and privacy concerns.
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
Federated learning is widely used in medical applications for training global models without needing local data access.
Learning to Check: Unleashing Potentials for Self-Correction in Large Language Models
In this paper, we aim to enhance the self-checking capabilities of LLMs by constructing training data for checking tasks.
TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processing
Representation learning frameworks in unlabeled time series have been proposed for medical signal processing.
Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning
To gain a better understanding of the training process and potential risks of text-to-image synthesis, we perform a systematic investigation of backdoor attack on text-to-image diffusion models and propose BadT2I, a general multimodal backdoor attack framework that tampers with image synthesis in diverse semantic levels.
NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning
At present, backdoor attacks attract attention as they do great harm to deep learning models.
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images
3D photography renders a static image into a video with appealing 3D visual effects.
Ranked #1 on
Image Outpainting
on MSCOCO
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning
Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages.
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks.
NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
In this paper, we present NUWA-Infinity, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or long-duration videos.
Ranked #1 on
Image Outpainting
on LHQC
NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
To cover language, image, and video at the same time for different scenarios, a 3D transformer encoder-decoder framework is designed, which can not only deal with videos as 3D data but also adapt to texts and images as 1D and 2D data, respectively.
Ranked #1 on
Text-to-Video Generation
on Kinetics
Hybrid Reasoning Network for Video-based Commonsense Captioning
Firstly, we develop multi-commonsense learning for semantic-level reasoning by jointly training different commonsense types in a unified network, which encourages the interaction between the clues of multiple commonsense descriptions, event-wise captions and videos.
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training
We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner.
Ranked #5 on
Image-to-Text Retrieval
on MS COCO
(Recall@10 metric)
