Multimodal Feature Extraction and Fusion for Emotional Reaction Intensity Estimation and Expression Classification in Videos with Transformers

In this paper, we present our solutions to the two sub-challenges of Affective Behavior Analysis in the wild (ABAW) 2023: the Emotional Reaction Intensity (ERI) Estimation Challenge and Expression (Expr) Classification Challenge. ABAW 2023 focuses on the problem of affective behavior analysis in the wild, with the goal of creating machines and robots that have the ability to understand human feelings, emotions and behaviors, which can effectively contribute to the advent of a more intelligent future. In our work, we use different models and tools for the Hume-Reaction dataset to extract features of various aspects, such as audio features, video features, etc. By analyzing, combining, and studying these multimodal features, we effectively improve the accuracy of the model for multimodal sentiment prediction. For the Emotional Reaction Intensity (ERI) Estimation Challenge, our method shows excellent results with a Pearson coefficient on the validation dataset, exceeding the baseline method by 84 percent.

Domain Re-Modulation for Few-Shot Generative Domain Adaptation

In this study, we investigate the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using one or a few reference images. Building upon previous research that has focused on Target-domain Consistency, Large Diversity, and Cross-domain Consistency, we conclude two additional desired properties for GDA: Memory and Domain Association. To meet these properties, we proposed a novel method Domain Re-Modulation (DoRM). Specifically, DoRM freezes the source generator and employs additional mapping and affine modules (M&A module) to capture the attributes of the target domain, resulting in a linearly combinable domain shift in style space. This allows for high-fidelity multi-domain and hybrid-domain generation by integrating multiple M&A modules in a single generator. DoRM is lightweight and easy to implement. Extensive experiments demonstrated the superior performance of DoRM on both one-shot and 10-shot GDA, both quantitatively and qualitatively. Additionally, for the first time, multi-domain and hybrid-domain generation can be achieved with a minimal storage cost by using a single model. The code will be available at https://github.com/wuyi2020/DoRM.

FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs

Data-Efficient GANs (DE-GANs), which aim to learn generative models with a limited amount of training data, encounter several challenges for generating high-quality samples. Since data augmentation strategies have largely alleviated the training instability, how to further improve the generative performance of DE-GANs becomes a hotspot. Recently, contrastive learning has shown the great potential of increasing the synthesis quality of DE-GANs, yet related principles are not well explored. In this paper, we revisit and compare different contrastive learning strategies in DE-GANs, and identify (i) the current bottleneck of generative performance is the discontinuity of latent space; (ii) compared to other contrastive learning strategies, Instance-perturbation works towards latent space continuity, which brings the major improvement to DE-GANs. Based on these observations, we propose FakeCLR, which only applies contrastive learning on perturbed fake samples, and devises three related training techniques: Noise-related Latent Augmentation, Diversity-aware Queue, and Forgetting Factor of Queue. Our experimental results manifest the new state of the arts on both few-shot generation and limited-data generation. On multiple datasets, FakeCLR acquires more than 15% FID improvement compared to existing DE-GANs. Code is available at https://github.com/iceli1007/FakeCLR.

Forcing the Whole Video as Background: An Adversarial Learning Strategy for Weakly Temporal Action Localization

With video-level labels, weakly supervised temporal action localization (WTAL) applies a localization-by-classification paradigm to detect and classify the action in untrimmed videos. Due to the characteristic of classification, class-specific background snippets are inevitably mis-activated to improve the discriminability of the classifier in WTAL. To alleviate the disturbance of background, existing methods try to enlarge the discrepancy between action and background through modeling background snippets with pseudo-snippet-level annotations, which largely rely on artificial hypotheticals. Distinct from the previous works, we present an adversarial learning strategy to break the limitation of mining pseudo background snippets. Concretely, the background classification loss forces the whole video to be regarded as the background by a background gradient reinforcement strategy, confusing the recognition model. Reversely, the foreground(action) loss guides the model to focus on action snippets under such conditions. As a result, competition between the two classification losses drives the model to boost its ability for action modeling. Simultaneously, a novel temporal enhancement network is designed to facilitate the model to construct temporal relation of affinity snippets based on the proposed strategy, for further improving the performance of action localization. Finally, extensive experiments conducted on THUMOS14 and ActivityNet1.2 demonstrate the effectiveness of the proposed method.

Data-Efficient Backdoor Attacks

Recent studies have proven that deep neural networks are vulnerable to backdoor attacks. Specifically, by mixing a small number of poisoned samples into the training set, the behavior of the trained model can be maliciously controlled. Existing attack methods construct such adversaries by randomly selecting some clean data from the benign set and then embedding a trigger into them. However, this selection strategy ignores the fact that each poisoned sample contributes inequally to the backdoor injection, which reduces the efficiency of poisoning. In this paper, we formulate improving the poisoned data efficiency by the selection as an optimization problem and propose a Filtering-and-Updating Strategy (FUS) to solve it. The experimental results on CIFAR-10 and ImageNet-10 indicate that the proposed method is effective: the same attack success rate can be achieved with only 47% to 75% of the poisoned sample volume compared to the random selection strategy. More importantly, the adversaries selected according to one setting can generalize well to other settings, exhibiting strong transferability.

A Comprehensive Survey on Data-Efficient GANs in Image Generation

Generative Adversarial Networks (GANs) have achieved remarkable achievements in image synthesis. These successes of GANs rely on large scale datasets, requiring too much cost. With limited training data, how to stable the training process of GANs and generate realistic images have attracted more attention. The challenges of Data-Efficient GANs (DE-GANs) mainly arise from three aspects: (i) Mismatch Between Training and Target Distributions, (ii) Overfitting of the Discriminator, and (iii) Imbalance Between Latent and Data Spaces. Although many augmentation and pre-training strategies have been proposed to alleviate these issues, there lacks a systematic survey to summarize the properties, challenges, and solutions of DE-GANs. In this paper, we revisit and define DE-GANs from the perspective of distribution optimization. We conclude and analyze the challenges of DE-GANs. Meanwhile, we propose a taxonomy, which classifies the existing methods into three categories: Data Selection, GANs Optimization, and Knowledge Sharing. Last but not the least, we attempt to highlight the current problems and the future directions.

A Statistical Difference Reduction Method for Escaping Backdoor Detection

Recent studies show that Deep Neural Networks (DNNs) are vulnerable to backdoor attacks. An infected model behaves normally on benign inputs, whereas its prediction will be forced to an attack-specific target on adversarial data. Several detection methods have been developed to distinguish inputs to defend against such attacks. The common hypothesis that these defenses rely on is that there are large statistical differences between the latent representations of clean and adversarial inputs extracted by the infected model. However, although it is important, comprehensive research on whether the hypothesis must be true is lacking. In this paper, we focus on it and study the following relevant questions: 1) What are the properties of the statistical differences? 2) How to effectively reduce them without harming the attack intensity? 3) What impact does this reduction have on difference-based defenses? Our work is carried out on the three questions. First, by introducing the Maximum Mean Discrepancy (MMD) as the metric, we identify that the statistical differences of multi-level representations are all large, not just the highest level. Then, we propose a Statistical Difference Reduction Method (SDRM) by adding a multi-level MMD constraint to the loss function during training a backdoor model to effectively reduce the differences. Last, three typical difference-based detection methods are examined. The F1 scores of these defenses drop from 90%-100% on the regularly trained backdoor models to 60%-70% on the models trained with SDRM on all two datasets, four model architectures, and four attack methods. The results indicate that the proposed method can be used to enhance existing attacks to escape backdoor detection algorithms.

Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search

Numerous studies have demonstrated that deep neural networks are easily misled by adversarial examples. Effectively evaluating the adversarial robustness of a model is important for its deployment in practical applications. Currently, a common type of evaluation is to approximate the adversarial risk of a model as a robustness indicator by constructing malicious instances and executing attacks. Unfortunately, there is an error (gap) between the approximate value and the true value. Previous studies manually design attack methods to achieve a smaller error, which is inefficient and may miss a better solution. In this paper, we establish the tightening of the approximation error as an optimization problem and try to solve it with an algorithm. More specifically, we first analyze that replacing the non-convex and discontinuous 0-1 loss with a surrogate loss, a necessary compromise in calculating the approximation, is one of the main reasons for the error. Then we propose AutoLoss-AR, the first method for searching loss functions for tightening the approximation error of adversarial risk. Extensive experiments are conducted in multiple settings. The results demonstrate the effectiveness of the proposed method: the best-discovered loss functions outperform the handcrafted baseline by 0.9%-2.9% and 0.7%-2.0% on MNIST and CIFAR-10, respectively. Besides, we also verify that the searched losses can be transferred to other settings and explore why they are better than the baseline by visualizing the local loss landscape.

Are High-Frequency Components Beneficial for Training of Generative Adversarial Networks

Advancements in Generative Adversarial Networks (GANs) have the ability to generate realistic images that are visually indistinguishable from real images. However, recent studies of the image spectrum have demonstrated that generated and real images share significant differences at high frequency. Furthermore, the high-frequency components invisible to human eyes affect the decision of CNNs and are related to the robustness of it. Similarly, whether the discriminator will be sensitive to the high-frequency differences, thus reducing the fitting ability of the generator to the low-frequency components is an open problem. In this paper, we demonstrate that the discriminator in GANs is sensitive to such high-frequency differences that can not be distinguished by humans and the high-frequency components of images are not conducive to the training of GANs. Based on these, we propose two preprocessing methods eliminating high-frequency differences in GANs training: High-Frequency Confusion (HFC) and High-Frequency Filter (HFF). The proposed methods are general and can be easily applied to most existing GANs frameworks with a fraction of the cost. The advanced performance of the proposed method is verified on multiple loss functions, network architectures, and datasets.