Search Results for author:
Found 14 papers, 8 papers with code
CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion
In response, we present Contrasting Gradient Inversion for Diffusion Models (CGI-DM), a novel method featuring vivid visual representations for digital copyright authentication.
Improving Adversarial Attacks on Latent Diffusion Model
We show that these attacks add an extra error to the score function of adversarial examples predicted by LDM.
Toward effective protection against diffusion based mimicry through score distillation
In this work, we present novel findings on attacking latent diffusion models (LDM) and propose new plug-and-play strategies for more effective protection.
What Makes Good Open-Vocabulary Detector: A Disassembling Perspective
A two-stage object detector includes a visual backbone, a region proposal network (RPN), and a region of interest (RoI) head.
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
Additionally, we propose a Prompt-Enhanced Learning (PEL) module that integrates semantic priors using knowledge-based prompts to boost the discriminative capacity of context features while ensuring separability between anomaly sub-classes.
Anomaly Detection In Surveillance Videos
Video Anomaly Detection
+1
Mist: Towards Improved Adversarial Examples for Diffusion Models
Diffusion Models (DMs) have empowered great success in artificial-intelligence-generated content, especially in artwork creation, yet raising new concerns in intellectual properties and copyright.
Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples
Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate novel paintings in a similar style.
Temporal and Contextual Transformer for Multi-Camera Editing of TV Shows
The ability to choose an appropriate camera view among multiple cameras plays a vital role in TV shows delivery.
Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection
Video anomaly detection is recently formulated as a multiple instance learning task under weak supervision, in which each video is treated as a bag of snippets to be determined whether contains anomalies.
Enhancing Distributional Stability among Sub-populations
shifts in prediction mechanisms ($Y|X$-shifts).
CCMB: A Large-scale Chinese Cross-modal Benchmark
In this work, we build a large-scale high-quality Chinese Cross-Modal Benchmark named CCMB for the research community, which contains the currently largest public pre-training dataset Zero and five human-annotated fine-tuning datasets for downstream tasks.
Ranked #3 on
Image Retrieval
on Flickr30k-CN
Audio-Guided Attention Network for Weakly Supervised Violence Detection
Detecting violence in video is a challenging task due to its complex scenarios and great intra-class variability.
Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms
In this framework, the environment can be easily configured to realize all kinds of RL tasks in the mainstream research.
A Multi-Variate Triple-Regression Forecasting Algorithm for Long-Term Customized Allergy Season Prediction
In this paper, we propose a novel multi-variate algorithm using a triple-regression methodology to predict the airborne-pollen allergy season that can be customized for each patient in the long term.
