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Found 11 papers, 2 papers with code
zydhjh4593@SMM4H’22: A Generic Pre-trained BERT-based Framework for Social Media Health Text Classification
This paper describes our proposed framework for the 10 text classification tasks of Task 1a, 2a, 2b, 3a, 4, 5, 6, 7, 8, and 9, in the Social Media Mining for Health (SMM4H) 2022.
MMToM-QA: Multimodal Theory of Mind Question Answering
Human ToM, on the other hand, is more than video or text understanding.
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years.
Backdooring Textual Inversion for Concept Censorship
Users can easily download the word embedding from public websites like Civitai and add it to their own stable diffusion model without fine-tuning for personalization.
Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models
Although recent personalization methods have democratized high-resolution image synthesis by enabling swift concept acquisition with minimal examples and lightweight computation, they also present an exploitable avenue for high accessible backdoor attacks.
Ensemble Reinforcement Learning: A Survey
By offering guidance for future scientific research and engineering applications, this survey significantly contributes to the advancement of ERL.
Dual-View Selective Instance Segmentation Network for Unstained Live Adherent Cells in Differential Interference Contrast Images
In this study, we developed a novel deep-learning algorithm called dual-view selective instance segmentation network (DVSISN) for segmenting unstained adherent cells in differential interference contrast (DIC) images.
Computation and Data Efficient Backdoor Attacks
To address such limitations, we propose a novel confidence-based scoring methodology, which can efficiently measure the contribution of each poisoning sample based on the distance posteriors.
Watermarking Pre-trained Encoders in Contrastive Learning
It is challenging to migrate existing watermarking techniques from the classification tasks to the contrastive learning scenario, as the owner of the encoder lacks the knowledge of the downstream tasks which will be developed from the encoder in the future.
Eliciting Truthful Reports with Partial Signals in Repeated Games
Namely, besides charging the player the reported value, the mechanism charges a penalty proportional to her inconsistent reports.
Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network
In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions.
