by jsendak | Jan 12, 2024 | Computer Science
Commentary: Reinforcement Learning for Antibody Design
The field of antibody-based therapeutics has seen tremendous advancements in recent years, with targeted antibodies showing promise as a personalized therapy approach. This is particularly exciting for complex and individualized diseases like cancer, where a one-size-fits-all treatment may not be sufficient. However, one significant challenge in this field is the enormous search space of amino acid sequences that are involved in antibody design.
In this study, the authors address this challenge by introducing a novel reinforcement learning method specifically tailored to antibody design. Reinforcement learning is a type of machine learning where an agent learns to make optimal decisions by interacting with its environment and receiving feedback in the form of rewards or punishments. By utilizing this method, the researchers were able to train their model to design high-affinity antibodies targeting multiple antigens.
One notable aspect of this study is that the reinforcement learning model was trained using both online interaction and offline datasets. Online interaction refers to the model designing antibodies in real-time and receiving feedback on their performance, while offline datasets consist of pre-existing knowledge on protein structures and interactions. By combining these two approaches, the model was able to leverage both real-time information and existing knowledge to improve its performance.
The results of the study are highly promising. The researchers’ approach outperformed existing methods on all tested antigens in the Absolut! database, indicating its effectiveness in designing high-affinity antibodies. This demonstrates the power of reinforcement learning in solving complex problems like antibody design, where the search space is vast and traditional methods may struggle to find optimal solutions.
Moving forward, this study opens up exciting possibilities for the field of antibody-based therapeutics. The novel reinforcement learning method introduced here could be further refined and applied to other challenging domains in drug design or personalized medicine. Additionally, as more data becomes available and computational power continues to improve, we can expect the performance of reinforcement learning models in antibody design to further improve.
In conclusion, this study presents a groundbreaking approach to antibody design using reinforcement learning. By addressing the challenges posed by the extensive search space of amino acid sequences, the researchers have demonstrated the potential of this method in designing high-affinity antibodies. This research contributes to the ongoing efforts in developing personalized therapies for complex diseases and paves the way for future advancements in this field.
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by jsendak | Jan 12, 2024 | Computer Science
Audio-visual deepfake detection scrutinizes manipulations in public video
using complementary multimodal cues. Current methods, which train on fused
multimodal data for multimodal targets face challenges due to uncertainties and
inconsistencies in learned representations caused by independent modality
manipulations in deepfake videos. To address this, we propose cross-modality
and within-modality regularization to preserve modality distinctions during
multimodal representation learning. Our approach includes an audio-visual
transformer module for modality correspondence and a cross-modality
regularization module to align paired audio-visual signals, preserving modality
distinctions. Simultaneously, a within-modality regularization module refines
unimodal representations with modality-specific targets to retain
modal-specific details. Experimental results on the public audio-visual
dataset, FakeAVCeleb, demonstrate the effectiveness and competitiveness of our
approach.
Audio-visual deepfake detection scrutinizes manipulations in public video using complementary multimodal cues
Deepfake videos have become a significant challenge in today’s digital landscape, and detecting these manipulations is crucial to maintaining trust in multimedia information systems. This article presents a novel approach to deepfake detection that leverages multimodal cues, combining both audio and visual information.
The use of multimodal data for training deepfake detection models has posed challenges due to uncertainties and inconsistencies in the learned representations. This is primarily caused by independent manipulations in different modalities within deepfake videos. To address this problem, the proposed approach incorporates cross-modality and within-modality regularization techniques.
Cross-modality regularization
The cross-modality regularization module aims to preserve modality distinctions during multimodal representation learning. It achieves this by aligning paired audio-visual signals, ensuring that the audio and visual components correspond appropriately. This alignment helps in identifying any inconsistencies that may arise from deepfake manipulations.
Within-modality regularization
The within-modality regularization module focuses on refining unimodal representations with modality-specific targets. By doing so, it retains modal-specific details and further enhances the ability to identify any manipulations. This module fine-tunes the representations to capture the nuances specific to each modality, such as acoustic patterns in audio and visual features in videos.
The proposed approach also employs an audio-visual transformer module for modality correspondence. This module plays a crucial role in ensuring that the audio and visual information aligns correctly, enabling more accurate detection of deepfake manipulations.
Experimental results on the FakeAVCeleb dataset demonstrate the effectiveness and competitiveness of the proposed approach. The use of complementary multimodal cues and the incorporation of cross-modality and within-modality regularization techniques significantly enhance the ability to scrutinize manipulations in public video.
From a broader perspective, this research contributes to the field of multimedia information systems, specifically in the domain of deepfake detection. The cross-disciplinary nature of this work combines concepts from multimedia analysis, artificial reality, augmented reality, and virtual realities. By leveraging multimodal cues, this research presents a robust approach to detecting deepfakes, addressing the challenges posed by independent modality manipulations.
In conclusion, the proposed approach for audio-visual deepfake detection demonstrates the importance of considering multiple modalities in multimedia analysis. Through the use of cross-modality and within-modality regularization techniques, more accurate and robust deepfake detection can be achieved, contributing to the advancement of multimedia information systems and related fields.
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by jsendak | Jan 12, 2024 | Computer Science
In this paper, the authors present a cosmetic-specific skin image dataset, which is a valuable contribution to the field of cosmetic rendering and image-to-image translation. The dataset consists of skin images from 45 patches, with 5 skin patches each from 9 participants. The size of each patch is 8mm x 8mm. These patches were captured using a novel capturing device inspired by Light Stage.
The use of a specialized capturing device is a significant improvement over existing methods for capturing skin images. By capturing over 600 images of each skin patch under diverse lighting conditions in just 30 seconds, the authors have been able to create a comprehensive dataset that captures the nuances of cosmetic products on different skin types and under various lighting conditions.
One of the strengths of this dataset is its focus on specific cosmetic products, namely foundation, blusher, and highlighter. This allows researchers and practitioners in the cosmetics industry to have a more targeted approach when it comes to analyzing and developing new rendering techniques for these particular products.
The authors then demonstrate the viability of the dataset by using it in an image-to-image translation-based pipeline for cosmetic rendering. This approach shows promise in accurately rendering how different cosmetic products would appear on different skin types. By comparing their data-driven approach to an existing cosmetic rendering method, the authors clearly demonstrate the advantages and improved results that can be achieved by using their dataset.
Overall, this paper provides a valuable resource for researchers and practitioners in the field of cosmetics. The dataset and the image-to-image translation pipeline introduce new possibilities for cosmetic rendering and provide a solid foundation for future research in this area. Furthermore, with the rapid growth of the cosmetics industry, datasets like these will be crucial in ensuring that digital representations of cosmetic products accurately reflect their real-world appearance on various skin types.
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by jsendak | Jan 12, 2024 | Computer Science
Audio-Visual Emotion Recognition (AVER) has garnered increasing attention in
recent years for its critical role in creating emotion-ware intelligent
machines. Previous efforts in this area are dominated by the supervised
learning paradigm. Despite significant progress, supervised learning is meeting
its bottleneck due to the longstanding data scarcity issue in AVER. Motivated
by recent advances in self-supervised learning, we propose Hierarchical
Contrastive Masked Autoencoder (HiCMAE), a novel self-supervised framework that
leverages large-scale self-supervised pre-training on vast unlabeled
audio-visual data to promote the advancement of AVER. Following prior arts in
self-supervised audio-visual representation learning, HiCMAE adopts two primary
forms of self-supervision for pre-training, namely masked data modeling and
contrastive learning. Unlike them which focus exclusively on top-layer
representations while neglecting explicit guidance of intermediate layers,
HiCMAE develops a three-pronged strategy to foster hierarchical audio-visual
feature learning and improve the overall quality of learned representations. To
verify the effectiveness of HiCMAE, we conduct extensive experiments on 9
datasets covering both categorical and dimensional AVER tasks. Experimental
results show that our method significantly outperforms state-of-the-art
supervised and self-supervised audio-visual methods, which indicates that
HiCMAE is a powerful audio-visual emotion representation learner. Codes and
models will be publicly available at https://github.com/sunlicai/HiCMAE.
Audio-Visual Emotion Recognition (AVER) is an important area in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. It involves the development of intelligent machines that can recognize and understand human emotions based on audio-visual data. The article highlights the limitations of the traditional supervised learning paradigm and proposes a novel self-supervised framework called Hierarchical Contrastive Masked Autoencoder (HiCMAE) to address the data scarcity issue in AVER.
The HiCMAE framework leverages large-scale self-supervised pre-training on unlabeled audio-visual data to enhance the performance of AVER systems. It adopts two primary forms of self-supervision: masked data modeling and contrastive learning. These techniques help in learning high-quality representations of audio-visual features.
What sets HiCMAE apart from previous approaches is its emphasis on hierarchical audio-visual feature learning. While previous methods focus only on top-layer representations, HiCMAE incorporates explicit guidance for intermediate layers. This three-pronged strategy enhances the overall quality of learned representations.
To validate the effectiveness of HiCMAE, extensive experiments are conducted on 9 datasets covering both categorical and dimensional AVER tasks. The experimental results demonstrate that HiCMAE outperforms state-of-the-art supervised and self-supervised audio-visual methods. This indicates that HiCMAE is a powerful audio-visual emotion representation learner, capable of improving the performance of AVER systems.
The multi-disciplinary nature of this content is evident in its connections to various fields. In multimedia information systems, HiCMAE contributes to the development of intelligent machines that can process and interpret audio-visual data in relation to human emotions. In animations, artificial reality, augmented reality, and virtual realities, HiCMAE can enable more realistic and immersive experiences by incorporating emotion recognition capabilities into virtual environments.
Overall, this article introduces a promising framework, HiCMAE, for enhancing Audio-Visual Emotion Recognition. Its self-supervised learning approach and hierarchical feature learning strategy address the limitations of data scarcity. The experimental results indicate its superiority over existing methods and highlight its potential for applications in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jan 12, 2024 | Computer Science
Robustness certification has become an essential aspect of neural networks, particularly for safety-critical applications. However, until now, certification methods have been limited to elementary architectures and benchmark datasets. In this paper, the authors address this limitation by focusing on the robustness certification of scene text recognition (STR) models, which involve complex image-based sequence prediction.
The authors propose STR-Cert, a novel certification method specifically designed for STR models. To do so, they extend the DeepPoly polyhedral verification framework and introduce new polyhedral bounds and algorithms for key components of STR models. This extension allows for the robustness certification of three types of STR model architectures, including the standard STR pipelines and the Vision Transformer.
One of the significant contributions of this work is the certification and comparison of STR models on six datasets. This not only demonstrates the efficiency and scalability of robustness certification but also provides valuable insights into the performance of different STR architectures. In particular, the authors highlight the effectiveness of the Vision Transformer in achieving robustness certification.
By addressing the robustness certification of STR models, this paper expands the scope of certification methods beyond basic architectures and benchmark datasets. The proposed STR-Cert method offers a promising approach to ensuring the reliability and safety of complex image-based sequence prediction systems. As robustness becomes increasingly critical in real-world applications, this research opens up new possibilities for certifying neural networks in diverse domains.
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by jsendak | Jan 12, 2024 | Computer Science
Due to the successful application of deep learning, audio spoofing detection
has made significant progress. Spoofed audio with speech synthesis or voice
conversion can be well detected by many countermeasures. However, an automatic
speaker verification system is still vulnerable to spoofing attacks such as
replay or Deep-Fake audio. Deep-Fake audio means that the spoofed utterances
are generated using text-to-speech (TTS) and voice conversion (VC) algorithms.
Here, we propose a novel framework based on hybrid features with the
self-attention mechanism. It is expected that hybrid features can be used to
get more discrimination capacity. Firstly, instead of only one type of
conventional feature, deep learning features and Mel-spectrogram features will
be extracted by two parallel paths: convolution neural networks and a
short-time Fourier transform (STFT) followed by Mel-frequency. Secondly,
features will be concatenated by a max-pooling layer. Thirdly, there is a
Self-attention mechanism for focusing on essential elements. Finally, ResNet
and a linear layer are built to get the results. Experimental results reveal
that the hybrid features, compared with conventional features, can cover more
details of an utterance. We achieve the best Equal Error Rate (EER) of 9.67%
in the physical access (PA) scenario and 8.94% in the Deep fake task on the
ASVspoof 2021 dataset. Compared with the best baseline system, the proposed
approach improves by 74.60% and 60.05%, respectively.
Analysis of the Content:
The content discusses the progress made in audio spoofing detection through the application of deep learning. It highlights that while many countermeasures can effectively detect spoofed audio created using speech synthesis or voice conversion, automatic speaker verification systems are still vulnerable to spoofing attacks such as replay or Deep-Fake audio.
To address this issue, the article proposes a novel framework based on hybrid features with the self-attention mechanism. The use of hybrid features, which include deep learning features and Mel-spectrogram features, is expected to provide more discrimination capacity.
- Parallel Feature Extraction: Instead of relying on only one type of conventional feature, the proposed framework extracts deep learning features and Mel-spectrogram features using two parallel paths: convolution neural networks and a short-time Fourier transform (STFT) followed by Mel-frequency.
- Max-Pooling and Concatenation: The extracted features are then concatenated using a max-pooling layer. This step helps combine the complementary information present in both types of features.
- Self-Attention Mechanism: The framework incorporates a self-attention mechanism, which allows the model to focus on essential elements in the features. This attention mechanism aids in capturing relevant details and enhancing discrimination ability.
- Model Architecture: The final step involves building a ResNet and a linear layer to process the concatenated feature representation and obtain the results.
The experimental results demonstrate the effectiveness of the proposed approach. The hybrid features outperform conventional features in terms of covering more details of the utterance. The Equal Error Rate (EER) achieved on the ASVspoof 2021 dataset shows significant improvements compared to the best baseline system, with a 74.60% improvement in the physical access (PA) scenario and a 60.05% improvement in the Deep fake task.
Multi-disciplinary Nature:
This content touches upon various aspects of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
1. Multimedia Information Systems: The research on audio spoofing detection relates to multimedia information systems, as it involves processing and analyzing audio data. The proposed framework showcases the integration of different features and deep learning techniques to enhance audio verification systems.
2. Animations: While not directly mentioned in the content, animations can play a role in audio spoofing detection. Deep-Fake audio typically involves combining synthesized speech with manipulated visuals to create realistic fraudulent content. Animations can contribute to the creation of visually convincing deep fakes.
3. Artificial Reality: Audio spoofing detection is a significant challenge in the realm of artificial reality, as it affects the authenticity and credibility of audio content used in virtual and augmented reality experiences. Ensuring the integrity of audio enhances the immersion and realism of artificial reality environments.
4. Augmented Reality: Augmented reality applications heavily rely on accurate audio representation to provide realistic audio overlays and spatial sound effects. By improving audio spoofing detection, the proposed framework contributes to enhancing the credibility of audio-based augmented reality experiences.
5. Virtual Realities: Virtual reality experiences aim to create immersive environments that stimulate multiple senses, including hearing. Detecting and mitigating audio spoofing attacks ensures that the virtual reality environment maintains a high level of realism and prevents manipulation of virtual audio sources.
Conclusion:
The content provides an overview of the progress made in audio spoofing detection and introduces a novel framework based on hybrid features and the self-attention mechanism. The proposed approach demonstrates improved discrimination capacity and outperforms conventional methods. The multi-disciplinary nature of the discussed concepts highlights their relevance to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. This research contributes to the broader field by addressing a crucial aspect of audio integrity in various multimedia applications.
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