Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm

Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific identities with just a single reference image. However, existing methods primarily integrate reference images within the text embedding space, leading to a complex entanglement of image and text information, which poses challenges for preserving both identity fidelity and semantic consistency. To tackle this challenge, we propose Infinite-ID, an ID-semantics decoupling paradigm for identity-preserved personalization. Specifically, we introduce identity-enhanced training, incorporating an additional image cross-attention module to capture sufficient ID information while deactivating the original text cross-attention module of the diffusion model. This ensures that the image stream faithfully represents the identity provided by the reference image while mitigating interference from textual input. Additionally, we introduce a feature interaction mechanism that combines a mixed attention module with an AdaIN-mean operation to seamlessly merge the two streams. This mechanism not only enhances the fidelity of identity and semantic consistency but also enables convenient control over the styles of the generated images. Extensive experimental results on both raw photo generation and style image generation demonstrate the superior performance of our proposed method.

Closed-Loop Unsupervised Representation Disentanglement with $β$-VAE Distillation and Diffusion Probabilistic Feedback

Representation disentanglement may help AI fundamentally understand the real world and thus benefit both discrimination and generation tasks. It currently has at least three unresolved core issues: (i) heavy reliance on label annotation and synthetic data -- causing poor generalization on natural scenarios; (ii) heuristic/hand-craft disentangling constraints make it hard to adaptively achieve an optimal training trade-off; (iii) lacking reasonable evaluation metric, especially for the real label-free data. To address these challenges, we propose a \textbf{C}losed-\textbf{L}oop unsupervised representation \textbf{Dis}entanglement approach dubbed \textbf{CL-Dis}. Specifically, we use diffusion-based autoencoder (Diff-AE) as a backbone while resorting to $\beta$-VAE as a co-pilot to extract semantically disentangled representations. The strong generation ability of diffusion model and the good disentanglement ability of VAE model are complementary. To strengthen disentangling, VAE-latent distillation and diffusion-wise feedback are interconnected in a closed-loop system for a further mutual promotion. Then, a self-supervised \textbf{Navigation} strategy is introduced to identify interpretable semantic directions in the disentangled latent space. Finally, a new metric based on content tracking is designed to evaluate the disentanglement effect. Experiments demonstrate the superiority of CL-Dis on applications like real image manipulation and visual analysis.

Diffusion model for relational inference

Dynamical behaviors of complex interacting systems, including brain activities, financial price movements, and physical collective phenomena, are associated with underlying interactions between the system's components. The issue of uncovering interaction relations in such systems using observable dynamics is called relational inference. In this study, we propose a Diffusion model for Relational Inference (DiffRI), inspired by a self-supervised method for probabilistic time series imputation. DiffRI learns to infer the probability of the presence of connections between components through conditional diffusion modeling. Experiments on both simulated and quasi-real datasets show that DiffRI is highly competent compared with other state-of-the-art models in discovering ground truth interactions in an unsupervised manner. Our code will be made public soon.

Two-stream joint matching method based on contrastive learning for few-shot action recognition

Although few-shot action recognition based on metric learning paradigm has achieved significant success, it fails to address the following issues: (1) inadequate action relation modeling and underutilization of multi-modal information; (2) challenges in handling video matching problems with different lengths and speeds, and video matching problems with misalignment of video sub-actions. To address these issues, we propose a Two-Stream Joint Matching method based on contrastive learning (TSJM), which consists of two modules: Multi-modal Contrastive Learning Module (MCL) and Joint Matching Module (JMM). The objective of the MCL is to extensively investigate the inter-modal mutual information relationships, thereby thoroughly extracting modal information to enhance the modeling of action relationships. The JMM aims to simultaneously address the aforementioned video matching problems. The effectiveness of the proposed method is evaluated on two widely used few shot action recognition datasets, namely, SSv2 and Kinetics. Comprehensive ablation experiments are also conducted to substantiate the efficacy of our proposed approach.

Efficient Trigger Word Insertion

With the boom in the natural language processing (NLP) field these years, backdoor attacks pose immense threats against deep neural network models. However, previous works hardly consider the effect of the poisoning rate. In this paper, our main objective is to reduce the number of poisoned samples while still achieving a satisfactory Attack Success Rate (ASR) in text backdoor attacks. To accomplish this, we propose an efficient trigger word insertion strategy in terms of trigger word optimization and poisoned sample selection. Extensive experiments on different datasets and models demonstrate that our proposed method can significantly improve attack effectiveness in text classification tasks. Remarkably, our approach achieves an ASR of over 90% with only 10 poisoned samples in the dirty-label setting and requires merely 1.5% of the training data in the clean-label setting.

Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation

Few-shot image generation aims to train generative models using a small number of training images. When there are few images available for training (e.g. 10 images), Learning From Scratch (LFS) methods often generate images that closely resemble the training data while Transfer Learning (TL) methods try to improve performance by leveraging prior knowledge from GANs pre-trained on large-scale datasets. However, current TL methods may not allow for sufficient control over the degree of knowledge preservation from the source model, making them unsuitable for setups where the source and target domains are not closely related. To address this, we propose a novel pipeline called Peer is your Pillar (PIP), which combines a target few-shot dataset with a peer dataset to create a data-unbalanced conditional generation. Our approach includes a class embedding method that separates the class space from the latent space, and we use a direction loss based on pre-trained CLIP to improve image diversity. Experiments on various few-shot datasets demonstrate the advancement of the proposed PIP, especially reduces the training requirements of few-shot image generation.

Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks

As the number of parameters in Deep Neural Networks (DNNs) scales, the thirst for training data also increases. To save costs, it has become common for users and enterprises to delegate time-consuming data collection to third parties. Unfortunately, recent research has shown that this practice raises the risk of DNNs being exposed to backdoor attacks. Specifically, an attacker can maliciously control the behavior of a trained model by poisoning a small portion of the training data. In this study, we focus on improving the poisoning efficiency of backdoor attacks from the sample selection perspective. The existing attack methods construct such poisoned samples by randomly selecting some clean data from the benign set and then embedding a trigger into them. However, this random selection strategy ignores that each sample may contribute differently to the backdoor injection, thereby reducing the poisoning efficiency. To address the above problem, a new selection strategy named Improved Filtering and Updating Strategy (FUS++) is proposed. Specifically, we adopt the forgetting events of the samples to indicate the contribution of different poisoned samples and use the curvature of the loss surface to analyses the effectiveness of this phenomenon. Accordingly, we combine forgetting events and curvature of different samples to conduct a simple yet efficient sample selection strategy. The experimental results on image classification (CIFAR-10, CIFAR-100, ImageNet-10), text classification (AG News), audio classification (ESC-50), and age regression (Facial Age) consistently demonstrate the effectiveness of the proposed strategy: the attack performance using FUS++ is significantly higher than that using random selection for the same poisoning ratio.

Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification

The Dissemination Process Classification (DPC) is a popular application of temporal graph classification. The aim of DPC is to classify different spreading patterns of information or pestilence within a community represented by discrete-time temporal graphs. Recently, a reservoir computing-based model named Dynamical Graph Echo State Network (DynGESN) has been proposed for processing temporal graphs with relatively high effectiveness and low computational costs. In this study, we propose a novel model which combines a novel data augmentation strategy called snapshot merging with the DynGESN for dealing with DPC tasks. In our model, the snapshot merging strategy is designed for forming new snapshots by merging neighboring snapshots over time, and then multiple reservoir encoders are set for capturing spatiotemporal features from merged snapshots. After those, the logistic regression is adopted for decoding the sum-pooled embeddings into the classification results. Experimental results on six benchmark DPC datasets show that our proposed model has better classification performances than the DynGESN and several kernel-based models.

Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios

Recent deep neural networks (DNNs) have come to rely on vast amounts of training data, providing an opportunity for malicious attackers to exploit and contaminate the data to carry out backdoor attacks. These attacks significantly undermine the reliability of DNNs. However, existing backdoor attack methods make unrealistic assumptions, assuming that all training data comes from a single source and that attackers have full access to the training data. In this paper, we address this limitation by introducing a more realistic attack scenario where victims collect data from multiple sources, and attackers cannot access the complete training data. We refer to this scenario as data-constrained backdoor attacks. In such cases, previous attack methods suffer from severe efficiency degradation due to the entanglement between benign and poisoning features during the backdoor injection process. To tackle this problem, we propose a novel approach that leverages the pre-trained Contrastive Language-Image Pre-Training (CLIP) model. We introduce three CLIP-based technologies from two distinct streams: Clean Feature Suppression, which aims to suppress the influence of clean features to enhance the prominence of poisoning features, and Poisoning Feature Augmentation, which focuses on augmenting the presence and impact of poisoning features to effectively manipulate the model's behavior. To evaluate the effectiveness, harmlessness to benign accuracy, and stealthiness of our method, we conduct extensive experiments on 3 target models, 3 datasets, and over 15 different settings. The results demonstrate remarkable improvements, with some settings achieving over 100% improvement compared to existing attacks in data-constrained scenarios. Our research contributes to addressing the limitations of existing methods and provides a practical and effective solution for data-constrained backdoor attacks.

A Proxy-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks

Poisoning efficiency is a crucial factor in poisoning-based backdoor attacks. Attackers prefer to use as few poisoned samples as possible to achieve the same level of attack strength, in order to remain undetected. Efficient triggers have significantly improved poisoning efficiency, but there is still room for improvement. Recently, selecting efficient samples has shown promise, but it requires a proxy backdoor injection task to find an efficient poisoned sample set, which can lead to performance degradation if the proxy attack settings are different from the actual settings used by the victims. In this paper, we propose a novel Proxy-Free Strategy (PFS) that selects efficient poisoned samples based on individual similarity and set diversity, effectively addressing this issue. We evaluate the proposed strategy on several datasets, triggers, poisoning ratios, architectures, and training hyperparameters. Our experimental results demonstrate that PFS achieves higher backdoor attack strength while x500 faster than previous proxy-based selection approaches.