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Found 19 papers, 9 papers with code
PLPP: Prompt Learning with Perplexity Is Self-Distillation for Vision-Language Models
A recent method, Context Optimization (CoOp), further improves the performance of VL models on downstream tasks by introducing prompt learning.
HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation
The task of layout-to-image generation involves synthesizing images based on the captions of objects and their spatial positions.
A Survey on Data Synthesis and Augmentation for Large Language Models
The success of Large Language Models (LLMs) is inherently linked to the availability of vast, diverse, and high-quality data for training and evaluation.
Revealing the Unseen: Guiding Personalized Diffusion Models to Expose Training Data
To answer this, we propose FineXtract, a framework for extracting fine-tuning data.
Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks
Through this modeling, we identify the primary cause of this corruption stage: a narrowed learning distribution inherent in the nature of few-shot fine-tuning.
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.
Targeted Attack Improves Protection against Unauthorized Diffusion Customization
In this paper, we propose a simple yet effective improvement for the protection against unauthorized diffusion customization by introducing targeted attacks.
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.
