SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields

Neural radiance fields (NeRF) has attracted considerable attention for their exceptional ability in synthesizing novel views with high fidelity. However, the presence of motion blur, resulting from slight camera movements during extended shutter exposures, poses a significant challenge, potentially compromising the quality of the reconstructed 3D scenes. While recent studies have addressed this issue, they do not consider the continuous dynamics of camera movements during image acquisition, leading to inaccurate scene reconstruction. Additionally, these methods are plagued by slow training and rendering speed. To effectively handle these issues, we propose sequential motion understanding radiance fields (SMURF), a novel approach that employs neural ordinary differential equation (Neural-ODE) to model continuous camera motion and leverages the explicit volumetric representation method for faster training and robustness to motion-blurred input images. The core idea of the SMURF is continuous motion blurring kernel (CMBK), a unique module designed to model a continuous camera movements for processing blurry inputs. Our model, rigorously evaluated against benchmark datasets, demonstrates state-of-the-art performance both quantitatively and qualitatively.

Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation

Referring Image Segmentation (RIS) aims to segment target objects expressed in natural language within a scene at the pixel level. Various recent RIS models have achieved state-of-the-art performance by generating contextual tokens to model multimodal features from pretrained encoders and effectively fusing them using transformer-based cross-modal attention. While these methods match language features with image features to effectively identify likely target objects, they often struggle to correctly understand contextual information in complex and ambiguous sentences and scenes. To address this issue, we propose a novel bidirectional token-masking autoencoder (BTMAE) inspired by the masked autoencoder (MAE). The proposed model learns the context of image-to-language and language-to-image by reconstructing missing features in both image and language features at the token level. In other words, this approach involves mutually complementing across the features of images and language, with a focus on enabling the network to understand interconnected deep contextual information between the two modalities. This learning method enhances the robustness of RIS performance in complex sentences and scenes. Our BTMAE achieves state-of-the-art performance on three popular datasets, and we demonstrate the effectiveness of the proposed method through various ablation studies.

Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation

Unsupervised video object segmentation (VOS) is a task that aims to detect the most salient object in a video without external guidance about the object. To leverage the property that salient objects usually have distinctive movements compared to the background, recent methods collaboratively use motion cues extracted from optical flow maps with appearance cues extracted from RGB images. However, as optical flow maps are usually very relevant to segmentation masks, the network is easy to be learned overly dependent on the motion cues during network training. As a result, such two-stream approaches are vulnerable to confusing motion cues, making their prediction unstable. To relieve this issue, we design a novel motion-as-option network by treating motion cues as optional. During network training, RGB images are randomly provided to the motion encoder instead of optical flow maps, to implicitly reduce motion dependency of the network. As the learned motion encoder can deal with both RGB images and optical flow maps, two different predictions can be generated depending on which source information is used as motion input. In order to fully exploit this property, we also propose an adaptive output selection algorithm to adopt optimal prediction result at test time. Our proposed approach affords state-of-the-art performance on all public benchmark datasets, even maintaining real-time inference speed.

On the Amplification of Linguistic Bias through Unintentional Self-reinforcement Learning by Generative Language Models -- A Perspective

Generative Language Models (GLMs) have the potential to significantly shape our linguistic landscape due to their expansive use in various digital applications. However, this widespread adoption might inadvertently trigger a self-reinforcement learning cycle that can amplify existing linguistic biases. This paper explores the possibility of such a phenomenon, where the initial biases in GLMs, reflected in their generated text, can feed into the learning material of subsequent models, thereby reinforcing and amplifying these biases. Moreover, the paper highlights how the pervasive nature of GLMs might influence the linguistic and cognitive development of future generations, as they may unconsciously learn and reproduce these biases. The implications of this potential self-reinforcement cycle extend beyond the models themselves, impacting human language and discourse. The advantages and disadvantages of this bias amplification are weighed, considering educational benefits and ease of future GLM learning against threats to linguistic diversity and dependence on initial GLMs. This paper underscores the need for rigorous research to understand and address these issues. It advocates for improved model transparency, bias-aware training techniques, development of methods to distinguish between human and GLM-generated text, and robust measures for fairness and bias evaluation in GLMs. The aim is to ensure the effective, safe, and equitable use of these powerful technologies, while preserving the richness and diversity of human language.

ZIGNeRF: Zero-shot 3D Scene Representation with Invertible Generative Neural Radiance Fields

Generative Neural Radiance Fields (NeRFs) have demonstrated remarkable proficiency in synthesizing multi-view images by learning the distribution of a set of unposed images. Despite the aptitude of existing generative NeRFs in generating 3D-consistent high-quality random samples within data distribution, the creation of a 3D representation of a singular input image remains a formidable challenge. In this manuscript, we introduce ZIGNeRF, an innovative model that executes zero-shot Generative Adversarial Network (GAN) inversion for the generation of multi-view images from a single out-of-domain image. The model is underpinned by a novel inverter that maps out-of-domain images into the latent code of the generator manifold. Notably, ZIGNeRF is capable of disentangling the object from the background and executing 3D operations such as 360-degree rotation or depth and horizontal translation. The efficacy of our model is validated using multiple real-image datasets: Cats, AFHQ, CelebA, CelebA-HQ, and CompCars.

DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion

Class-conditional image generation using generative adversarial networks (GANs) has been investigated through various techniques; however, it continues to face challenges such as mode collapse, training instability, and low-quality output in cases of datasets with high intra-class variation. Furthermore, most GANs often converge in larger iterations, resulting in poor iteration efficacy in training procedures. While Diffusion-GAN has shown potential in generating realistic samples, it has a critical limitation in generating class-conditional samples. To overcome these limitations, we propose a novel approach for class-conditional image generation using GANs called DuDGAN, which incorporates a dual diffusion-based noise injection process. Our method consists of three unique networks: a discriminator, a generator, and a classifier. During the training process, Gaussian-mixture noises are injected into the two noise-aware networks, the discriminator and the classifier, in distinct ways. This noisy data helps to prevent overfitting by gradually introducing more challenging tasks, leading to improved model performance. As a result, our method outperforms state-of-the-art conditional GAN models for image generation in terms of performance. We evaluated our method using the AFHQ, Food-101, and CIFAR-10 datasets and observed superior results across metrics such as FID, KID, Precision, and Recall score compared with comparison models, highlighting the effectiveness of our approach.

Game-Theoretical Analysis of Reviewer Rewards in Peer-Review Journal Systems: Analysis and Experimental Evaluation using Deep Reinforcement Learning

In this paper, we navigate the intricate domain of reviewer rewards in open-access academic publishing, leveraging the precision of mathematics and the strategic acumen of game theory. We conceptualize the prevailing voucher-based reviewer reward system as a two-player game, subsequently identifying potential shortcomings that may incline reviewers towards binary decisions. To address this issue, we propose and mathematically formalize an alternative reward system with the objective of mitigating this bias and promoting more comprehensive reviews. We engage in a detailed investigation of the properties and outcomes of both systems, employing rigorous game-theoretical analysis and deep reinforcement learning simulations. Our results underscore a noteworthy divergence between the two systems, with our proposed system demonstrating a more balanced decision distribution and enhanced stability. This research not only augments the mathematical understanding of reviewer reward systems, but it also provides valuable insights for the formulation of policies within journal review system. Our contribution to the mathematical community lies in providing a game-theoretical perspective to a real-world problem and in the application of deep reinforcement learning to simulate and understand this complex system.

GELU Activation Function in Deep Learning: A Comprehensive Mathematical Analysis and Performance

Selecting the most suitable activation function is a critical factor in the effectiveness of deep learning models, as it influences their learning capacity, stability, and computational efficiency. In recent years, the Gaussian Error Linear Unit (GELU) activation function has emerged as a dominant method, surpassing traditional functions such as the Rectified Linear Unit (ReLU) in various applications. This study presents a rigorous mathematical investigation of the GELU activation function, exploring its differentiability, boundedness, stationarity, and smoothness properties in detail. Additionally, we conduct an extensive experimental comparison of the GELU function against a broad range of alternative activation functions, utilizing a residual convolutional network trained on the CIFAR-10, CIFAR-100, and STL-10 datasets as the empirical testbed. Our results demonstrate the superior performance of GELU compared to other activation functions, establishing its suitability for a wide range of deep learning applications. This comprehensive study contributes to a more profound understanding of the underlying mathematical properties of GELU and provides valuable insights for practitioners aiming to select activation functions that optimally align with their specific objectives and constraints in deep learning.

Adaptive Graph Convolution Module for Salient Object Detection

Salient object detection (SOD) is a task that involves identifying and segmenting the most visually prominent object in an image. Existing solutions can accomplish this use a multi-scale feature fusion mechanism to detect the global context of an image. However, as there is no consideration of the structures in the image nor the relations between distant pixels, conventional methods cannot deal with complex scenes effectively. In this paper, we propose an adaptive graph convolution module (AGCM) to overcome these limitations. Prototype features are initially extracted from the input image using a learnable region generation layer that spatially groups features in the image. The prototype features are then refined by propagating information between them based on a graph architecture, where each feature is regarded as a node. Experimental results show that the proposed AGCM dramatically improves the SOD performance both quantitatively and quantitatively.

Guided Slot Attention for Unsupervised Video Object Segmentation

Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However, the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue, we propose a guided slot attention network to reinforce spatial structural information and obtain better foreground--background separation. The foreground and background slots, which are initialized with query guidance, are iteratively refined based on interactions with template information. Furthermore, to improve slot--template interaction and effectively fuse global and local features in the target and reference frames, K-nearest neighbors filtering and a feature aggregation transformer are introduced. The proposed model achieves state-of-the-art performance on two popular datasets. Additionally, we demonstrate the robustness of the proposed model in challenging scenes through various comparative experiments.