by jsendak | Jan 12, 2024 | Computer Science
Generalized Class Discovery (GCD) is a crucial task in machine learning that aims to identify both known and unknown classes from unlabeled datasets. However, existing GCD methods often assume that the occurrence of categories in the unlabeled data is evenly distributed, which is not the case in real-world scenarios. In natural environments, visual classes typically exhibit a long-tailed distribution, with some categories being much more prevalent than others.
This article introduces the concept of Long-tailed Generalized Category Discovery (Long-tailed GCD), which addresses the limitations of prevailing GCD methods by taking into account the imbalanced nature of real-world unlabeled datasets. The authors propose a robust methodology that incorporates two strategic regularizations to tackle the unique challenges posed by long-tailed GCD.
First, the authors propose a reweighting mechanism that increases the prominence of less-represented, tail-end categories. This approach acknowledges that rare categories are often vital but overlooked in traditional GCD methods. By assigning higher weights to these underrepresented categories during training, the proposed method ensures that they receive sufficient attention and are not overshadowed by the more frequent categories.
Second, a class prior constraint is introduced, which aligns with the expected class distribution in long-tailed datasets. This constraint takes into account the knowledge that certain categories are more likely to occur than others and incorporates this information into the GCD framework.
To evaluate the effectiveness of their proposed method, the authors conducted comprehensive experiments on two benchmark datasets: ImageNet100 and CIFAR100. The results demonstrated that their method outperformed previous state-of-the-art GCD methods by achieving an improvement of approximately 6-9% on ImageNet100. Furthermore, it achieved competitive performance on CIFAR100, demonstrating its effectiveness in handling long-tailed GCD scenarios.
Overall, this research makes a significant contribution to the field of Generalized Class Discovery by addressing the limitations of existing methods in handling long-tailed datasets. The proposed methodology, with its reweighting mechanism and class prior constraint, provides a more accurate and robust approach for discovering categories in real-world unlabeled datasets. Future research could explore further enhancements to this approach, such as incorporating additional information sources or adapting it to other domains beyond computer vision.
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by jsendak | Jan 12, 2024 | Computer Science
Self-supervised representation learning for human action recognition has
developed rapidly in recent years. Most of the existing works are based on
skeleton data while using a multi-modality setup. These works overlooked the
differences in performance among modalities, which led to the propagation of
erroneous knowledge between modalities while only three fundamental modalities,
i.e., joints, bones, and motions are used, hence no additional modalities are
explored.
In this work, we first propose an Implicit Knowledge Exchange Module (IKEM)
which alleviates the propagation of erroneous knowledge between low-performance
modalities. Then, we further propose three new modalities to enrich the
complementary information between modalities. Finally, to maintain efficiency
when introducing new modalities, we propose a novel teacher-student framework
to distill the knowledge from the secondary modalities into the mandatory
modalities considering the relationship constrained by anchors, positives, and
negatives, named relational cross-modality knowledge distillation. The
experimental results demonstrate the effectiveness of our approach, unlocking
the efficient use of skeleton-based multi-modality data. Source code will be
made publicly available at https://github.com/desehuileng0o0/IKEM.
Self-supervised representation learning for human action recognition has seen significant advancements in recent years. While most existing works in this field have focused on skeleton data and utilized a multi-modality setup, they have overlooked the variations in performance among different modalities. As a result, erroneous knowledge can be propagated between modalities. Additionally, these works have mainly explored three fundamental modalities: joints, bones, and motions, without investigating additional modalities.
In order to address these limitations, the authors of this work propose an Implicit Knowledge Exchange Module (IKEM). This module aims to mitigate the propagation of erroneous knowledge between low-performance modalities. Moreover, the authors introduce three new modalities to enhance the complementary information between different modalities.
To ensure efficiency while incorporating new modalities, the authors also present a novel teacher-student framework called relational cross-modality knowledge distillation. This framework allows for the transfer of knowledge from secondary modalities to mandatory modalities based on anchor points, positive examples, and negative examples.
This work’s experimental results demonstrate the effectiveness of the proposed approach in leveraging skeleton-based multi-modality data efficiently for human action recognition. By addressing the limitations of previous approaches and introducing novel techniques, this research contributes to the wider field of multimedia information systems, with a specific focus on animations, artificial reality, augmented reality, and virtual realities.
The concepts explored in this work highlight the multi-disciplinary nature of multimedia information systems. The integration of various modalities and the development of novel frameworks require expertise in computer vision, machine learning, human-computer interaction, and graphics. Moreover, the proposed IKEM module and relational cross-modality knowledge distillation framework provide valuable insights into how knowledge can be effectively exchanged and distilled across different modalities. These insights can potentially be applied to other domains within multimedia information systems, such as object recognition, scene understanding, and video analysis.
In conclusion, this work contributes to the advancement of human action recognition using a multi-modality approach. By addressing the limitations of previous works, introducing new modalities, and proposing novel frameworks, this research provides valuable insights into the efficient utilization of skeleton-based multi-modality data. The concepts discussed in this work have implications for the broader field of multimedia information systems, including areas such as animations, artificial reality, augmented reality, and virtual realities.
Source code: https://github.com/desehuileng0o0/IKEM
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by jsendak | Jan 12, 2024 | Computer Science
Accurate prediction of RNA secondary structure is crucial for understanding the intricate mechanisms involved in cellular regulation and disease processes. While traditional algorithms have been used in the past for this purpose, deep learning (DL) methods have taken the lead by successfully predicting complex features such as pseudoknots and multi-interacting base pairs.
However, one of the challenges faced in evaluating these DL methods lies in the difficulty of handling tertiary interactions in RNA structures. The traditional distance measures that are commonly used are not well-equipped to handle such interactions. Additionally, the evaluation measures currently used, such as F1 score and MCC (Matthews correlation coefficient), have their own limitations.
In this article, the Weisfeiler-Lehman graph kernel (WL) is proposed as an alternative metric for evaluating RNA structure prediction algorithms. By embracing graph-based metrics like WL, researchers can achieve fair and accurate evaluations. The use of WL as a metric not only provides a better evaluation framework for RNA structure prediction algorithms, but also offers valuable insights and guidance.
An RNA design experiment demonstrated the informative nature of WL as a guidance tool. With WL, researchers can gain a deeper understanding of the predicted RNA structures and make more informed decisions in the design process.
This article highlights the importance of accurate evaluation in RNA structure prediction and the role that graph-based metrics like WL can play in improving this evaluation process. By using WL as an alternative metric, researchers can achieve more comprehensive and insightful assessments of their prediction algorithms, ultimately leading to advancements in our understanding of cellular regulation and disease mechanisms.
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by jsendak | Jan 12, 2024 | Computer Science
In this paper, we present a multimodal and dynamical VAE (MDVAE) applied to
unsupervised audio-visual speech representation learning. The latent space is
structured to dissociate the latent dynamical factors that are shared between
the modalities from those that are specific to each modality. A static latent
variable is also introduced to encode the information that is constant over
time within an audiovisual speech sequence. The model is trained in an
unsupervised manner on an audiovisual emotional speech dataset, in two stages.
In the first stage, a vector quantized VAE (VQ-VAE) is learned independently
for each modality, without temporal modeling. The second stage consists in
learning the MDVAE model on the intermediate representation of the VQ-VAEs
before quantization. The disentanglement between static versus dynamical and
modality-specific versus modality-common information occurs during this second
training stage. Extensive experiments are conducted to investigate how
audiovisual speech latent factors are encoded in the latent space of MDVAE.
These experiments include manipulating audiovisual speech, audiovisual facial
image denoising, and audiovisual speech emotion recognition. The results show
that MDVAE effectively combines the audio and visual information in its latent
space. They also show that the learned static representation of audiovisual
speech can be used for emotion recognition with few labeled data, and with
better accuracy compared with unimodal baselines and a state-of-the-art
supervised model based on an audiovisual transformer architecture.
In this article, we are introduced to the concept of multimodal and dynamical Variational Autoencoder (MDVAE) applied to unsupervised audio-visual speech representation learning. The key idea behind MDVAE is to structure the latent space in a way that it captures the shared and specific information between the audio and visual modalities, as well as the temporal dynamics within an audiovisual speech sequence.
This research is a testament to the multi-disciplinary nature of multimedia information systems, as it combines techniques from computer vision, machine learning, and speech processing. By integrating both audio and visual modalities, this approach paves the way for more immersive and realistic multimedia experiences.
Animations, artificial reality, augmented reality, and virtual realities are all fields that greatly benefit from advancements in audio-visual processing. By effectively combining audio and visual information in the latent space, MDVAE opens up possibilities for creating more realistic and interactive virtual environments. Imagine a virtual reality game where characters not only look real but also sound realistic when they speak. This level of fidelity can greatly enhance the user’s immersion and overall experience.
Furthermore, this research addresses the challenge of disentangling static versus dynamical and modality-specific versus modality-common information. This is crucial for tasks such as audiovisual facial image denoising and emotion recognition. By learning a static representation of audiovisual speech, the model can effectively filter out noise and extract meaningful features that contribute to emotion recognition. The results demonstrate that MDVAE outperforms unimodal baselines and even a state-of-the-art supervised model based on an audiovisual transformer architecture.
Overall, this research showcases the potential of incorporating multimodal and dynamical approaches in the field of multimedia information systems. By harnessing the power of both audio and visual modalities, we can create more immersive experiences and improve tasks such as animation, artificial reality, augmented reality, and virtual realities. The MDVAE model’s ability to disentangle different factors opens up possibilities for various applications, including emotion recognition and facial image denoising.
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by jsendak | Jan 12, 2024 | Computer Science
Differentiable rendering is a technique that has gained importance in the field of visual computing applications. It involves representing a 3D scene as a model that is trained from 2D images using gradient descent. This allows for the generation of high-quality, photo-realistic imagery at high speeds.
Recent works, such as 3D Gaussian Splatting, have utilized a rasterization pipeline to enable the rendering of these learned 3D models. These methods have shown great promise and have achieved state-of-the-art quality for many important tasks.
However, one of the challenges in training these models is the computation of gradients, which is a significant bottleneck on GPUs. The large number of atomic operations involved in this process overwhelms the atomic units in the L2 partitions, leading to stalls.
In order to address this challenge, the authors of this work propose DISTWAR, a software approach to accelerate atomic operations. DISTWAR leverages two key ideas. Firstly, it enables warp-level reduction of threads at the SM sub-cores using registers, taking advantage of the locality in intra-warp atomic updates. Secondly, it distributes the atomic computation between the warp-level reduction at the SM and the L2 atomic units, increasing the throughput of atomic computation.
To implement DISTWAR, existing warp-level primitives are utilized. The authors evaluate DISTWAR on widely used raster-based differentiable rendering workloads and demonstrate significant speedups of 2.44x on average, with some cases achieving up to 5.7x speedup.
Expert Analysis
This work presents a novel approach to address a critical bottleneck in differentiable rendering: the computation of gradients during training. By leveraging warp-level reduction and distributing atomic computation between the SM and L2 atomic units, DISTWAR offers significant speed improvements.
One of the key advantages of DISTWAR is that it is a software-based solution, which means it can be easily integrated into existing rendering pipelines without the need for hardware modifications. This makes it a practical and accessible solution for a wide range of applications.
Furthermore, the evaluation of DISTWAR on various differentiable rendering workloads demonstrates its effectiveness across different scenarios. The significant speedups achieved highlight the potential impact of this approach in improving the efficiency of training 3D scene models.
However, it is worth noting that while DISTWAR provides notable speed improvements, it does not completely eliminate the computational cost associated with training differentiable rendering models. There is still a need for further research to explore other techniques and optimizations to further enhance the efficiency of this process.
Future Directions
Building on the foundations laid by DISTWAR, there are several potential avenues for future research in the field of differentiable rendering. One possible direction is the exploration of hardware-level optimizations specifically designed to accelerate the computation of gradients. By developing specialized hardware units or frameworks tailored to this task, it may be possible to achieve even greater speed improvements.
Another area of interest could be the investigation of alternative methods for representing 3D scenes in differentiable rendering. While the current approach relies on training models from 2D images, there may be possibilities for exploring other forms of data representation that can offer more efficient training processes.
Additionally, further work can be done to generalize the techniques proposed by DISTWAR to other domains and applications within visual computing. By expanding the scope of its application, DISTWAR has the potential to make a significant impact in accelerating a wide range of visual computing tasks beyond differentiable rendering.
In conclusion, the work on DISTWAR offers a valuable contribution to the field of differentiable rendering by addressing a critical bottleneck in training. With its software-based approach, it provides a practical solution for accelerating the computation of gradients and offers notable speed improvements. Further research and exploration of hardware-level optimizations and alternative data representation methods can pave the way for even more efficient training processes in the future.
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by jsendak | Jan 12, 2024 | Computer Science
In this new digital era, accessibility to real-world events is moving towards
web-based modules. This is mostly visible on e-commerce websites where there is
limited availability of physical verification. With this unforeseen
development, we depend on the verification in the virtual world to influence
our decisions. One of the decision making process is deeply based on review
reading. Reviews play an important part in this transactional process. And
seeking a real review can be very tenuous work for the user. On the other hand,
fake review heavily impacts these transaction records of a product. The article
presents an implementation of a Siamese network for detecting fake reviews. The
fake reviews dataset, consisting of 40K reviews, preprocessed with different
techniques. The cleaned data is passed through embeddings generated by MiniLM
BERT for contextual relationship and Word2Vec for semantic relationship to form
vectors. Further, the embeddings are trained in a Siamese network with LSTM
layers connected to fuzzy logic for decision-making. The results show that fake
reviews can be detected with high accuracy on a siamese network for prediction
and verification.
Analysis of Siamese Network for Detecting Fake Reviews
In today’s digital world, where accessibility to physical verification is often limited, we rely heavily on virtual platforms and online reviews to make informed decisions. However, the presence of fake reviews poses a significant challenge in this transactional process. Identifying genuine reviews from fake ones is crucial for users to make reliable choices.
The article presents an implementation of a Siamese network, a deep learning model known for its effectiveness in measuring similarities between inputs, for detecting fake reviews. The dataset used consists of 40,000 reviews, which have been preprocessed using various techniques to clean the data and make it suitable for analysis.
In order to capture the contextual and semantic relationships within the reviews, the cleaned data is transformed into numerical vectors using embeddings generated by MiniLM BERT and Word2Vec models. These embeddings capture the essence of the text and enable more meaningful comparisons between reviews.
The Siamese network architecture, with its LSTM layers, is then trained using the generated embeddings. The network is designed to extract relevant features from the review vectors and make predictions on whether a review is genuine or fake. The decision-making process is further enhanced by incorporating fuzzy logic, which allows for more nuanced analysis and decision rules based on the network’s outputs.
The results of the implementation demonstrate that fake reviews can be accurately detected with high precision using a Siamese network. This approach leverages the power of deep learning, natural language processing, and fuzzy logic to uncover patterns and anomalies in review data that are difficult to discern manually.
From a multidisciplinary perspective, this implementation highlights the seamless integration of concepts from various fields such as information retrieval, natural language processing, and artificial intelligence. The use of embeddings generated by MiniLM BERT and Word2Vec models showcases the importance of natural language understanding in deciphering the contextual and semantic relationships within textual data. The incorporation of fuzzy logic further emphasizes the role of computational intelligence in decision-making processes.
In the broader field of multimedia information systems, this implementation aligns with the growing demand for reliable and trustworthy information in the digital era. By leveraging advanced technologies like deep learning and artificial intelligence, it contributes to enhancing the quality and credibility of online platforms by detecting and filtering out fake reviews.
Moreover, this approach can also be applied in the domains of animations, artificial reality, augmented reality, and virtual realities, where user-generated content plays a crucial role. By automating the detection of fake reviews, content creators and platform providers can maintain the integrity and authenticity of their digital environments.
In conclusion, the implementation of a Siamese network for detecting fake reviews showcases the power of deep learning and multidisciplinary approaches in addressing real-world challenges. As technology continues to advance, such solutions will play a significant role in ensuring the reliability and transparency of digital transactions and interactions.
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