Modality-Aware Discriminative Fusion Network for Integrated Analysis of Brain Imaging Genomics.

Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD), characterized by subtle clinical symptoms that pose challenges for accurate diagnosis. The quest for the identification of MCI individuals has highlighted the importance of comprehending the underlying mechanisms of disease causation. Integrated analysis of brain imaging and genomics offers a promising avenue for predicting MCI risk before clinical symptom onset.

3D Snapshot: Invertible Embedding of 3D Neural Representations in a Single Image.

3D neural rendering enables photo-realistic reconstruction of a specific scene by encoding discontinuous inputs into a neural representation. Despite the remarkable rendering results, the storage of network parameters is not transmission-friendly and not extendable to metaverse applications. In this paper, we propose an invertible neural rendering approach that enables generating an interactive 3D model from a single image (i.

DreamAnime: Learning Style-Identity Textual Disentanglement for Anime and Beyond.

Text-to-image generation models have significantly broadened the horizons of creative expression through the power of natural language. However, navigating these models to generate unique concepts, alter their appearance, or reimagine them in unfamiliar roles presents an intricate challenge. For instance, how can we exploit language-guided models to transpose an anime character into a different art style, or envision a beloved character in a radically different setting or role? This paper unveils a novel approach named DreamAnime, designed to provide this level of creative freedom.

Monocular BEV Perception of Road Scenes via Front-to-Top View Projection.

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content.

Learning an Interpretable Stylized Subspace for 3D-aware Animatable Artforms.

Throughout history, static paintings have captivated viewers within display frames, yet the possibility of making these masterpieces vividly interactive remains intriguing. This research paper introduces 3DArtmator, a novel approach that aims to represent artforms in a highly interpretable stylized space, enabling 3D-aware animatable reconstruction and editing. Our rationale is to transfer the interpretability and 3D controllability of the latent space in a 3D-aware GAN to a stylized sub-space of a customized GAN, revitalizing the original artforms.

A dual-subcellular localized β-glucosidase confers pathogen and insect resistance without a yield penalty in maize.

Maize is one of the most important crops for food, cattle feed and energy production. However, maize is frequently attacked by various pathogens and pests, which pose a significant threat to maize yield and quality. Identification of quantitative trait loci and genes for resistance to pests will provide the basis for resistance breeding in maize.

The plant immune receptor SNC1 monitors helper NLRs targeted by a bacterial effector.

Plants deploy intracellular receptors to counteract pathogen effectors that suppress cell-surface-receptor-mediated immunity. To what extent pathogens manipulate intracellular receptor-mediated immunity, and how plants tackle such manipulation, remains unknown. Arabidopsis thaliana encodes three similar ADR1 class helper nucleotide-binding domain leucine-rich repeat receptors (ADR1, ADR1-L1, and ADR1-L2), which are crucial in plant immunity initiated by intracellular receptors.

Monocular Depth Estimation for Glass Walls With Context: A New Dataset and Method.

Traditional monocular depth estimation assumes that all objects are reliably visible in the RGB color domain. However, this is not always the case as more and more buildings are decorated with transparent glass walls. This problem has not been explored due to the difficulties in annotating the depth levels of glass walls, as commercial depth sensors cannot provide correct feedbacks on transparent objects.

Reducing Spatial Labeling Redundancy for Active Semi-Supervised Crowd Counting.

Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to reduce the labeling efforts. Given a limited labeling budget, they typically select a few crowd images and densely label all individuals in each of them.

Appearance-Preserved Portrait-to-Anime Translation via Proxy-Guided Domain Adaptation.

Converting a human portrait to anime style is a desirable but challenging problem. Existing methods fail to resolve this problem due to the large inherent gap between two domains that cannot be overcome by a simple direct mapping. For this reason, these methods struggle to preserve the appearance features in the original photo.

Fully Deformable Network for Multiview Face Image Synthesis.

Photorealistic multiview face synthesis from a single image is a challenging problem. Existing works mainly learn a texture mapping model from the source to the target faces. However, they rarely consider the geometric constraints on the internal deformation arising from pose variations, which causes a high level of uncertainty in face pose modeling, and hence, produces inferior results for large pose variations.

Self-Supervised Matting-Specific Portrait Enhancement and Generation.

We resolve the ill-posed alpha matting problem from a completely different perspective. Given an input portrait image, instead of estimating the corresponding alpha matte, we focus on the other end, to subtly enhance this input so that the alpha matte can be easily estimated by any existing matting models. This is accomplished by exploring the latent space of GAN models.

SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance.

Reliable nasopharyngeal carcinoma (NPC) segmentation plays an important role in radiotherapy planning. However, recent deep learning methods fail to achieve satisfactory NPC segmentation in magnetic resonance (MR) images, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable border, making it indiscernible from tightly connected surrounding tissues from immense and complex backgrounds. To address such background dominance problems, this paper proposes a sequential method (SeqSeg) to achieve accurate NPC segmentation.

Pro-PULSE: Learning Progressive Encoders of Latent Semantics in GANs for Photo Upsampling.

The state-of-the-art photo upsampling method, PULSE, demonstrates that a sharp, high-resolution (HR) version of a given low-resolution (LR) input can be obtained by exploring the latent space of generative models. However, mapping an extreme LR input (16) directly to an HR image (1024) is too ambiguous to preserve faithful local facial semantics. In this paper, we propose an enhanced upsampling approach, Pro-PULSE, that addresses the issues of semantic inconsistency and optimization complexity.

Erratum to "Multi-View Face Synthesis via Progressive Face Flow".

In the above article [1], unfortunately, Fig. 5 was not displayed correctly with many empty images. The correct version is supplemented here.

Deep Multiview Clustering via Iteratively Self-Supervised Universal and Specific Space Learning.

Multiview clustering seeks to partition objects via leveraging cross-view relations to provide a comprehensive description of the same objects. Most existing methods assume that different views are linear transformable or merely sampling from a common latent space. Such rigid assumptions betray reality, thus leading to unsatisfactory performance.

Multi-View Face Synthesis via Progressive Face Flow.

Existing GAN-based multi-view face synthesis methods rely heavily on "creating" faces, and thus they struggle in reproducing the faithful facial texture and fail to preserve identity when undergoing a large angle rotation. In this paper, we combat this problem by dividing the challenging large-angle face synthesis into a series of easy small-angle rotations, and each of them is guided by a face flow to maintain faithful facial details. In particular, we propose a Face Flow-guided Generative Adversarial Network (FFlowGAN) that is specifically trained for small-angle synthesis.

Self-Supervised Video Representation Learning by Uncovering Spatio-Temporal Statistics.

This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc. Then a neural network is built and trained to yield the statistical summaries given the video frames as inputs.

CrowdGAN: Identity-Free Interactive Crowd Video Generation and Beyond.

In this paper, we introduce a novel yet challenging research problem, interactive crowd video generation, committed to producing diverse and continuous crowd video, and relieving the difficulty of insufficient annotated real-world datasets in crowd analysis. Our goal is to recursively generate realistic future crowd video frames given few context frames, under the user-specified guidance, namely individual positions of the crowd. To this end, we propose a deep network architecture specifically designed for crowd video generation that is composed of two complementary modules, each of which combats the problems of crowd dynamic synthesis and appearance preservation respectively.

Real-Time Hierarchical Supervoxel Segmentation via a Minimum Spanning Tree.

Supervoxel segmentation algorithm has been applied as a preprocessing step for many vision tasks. However, existing supervoxel segmentation algorithms cannot generate hierarchical supervoxel segmentation well preserving the spatiotemporal boundaries in real time, which prevents the downstream applications from accurate and efficient processing. In this paper, we propose a real-time hierarchical supervoxel segmentation algorithm based on the minimum spanning tree (MST), which achieves state-of-the-art accuracy meanwhile at least 11× faster than existing methods.

Boundary-aware RGBD Salient Object Detection with Cross-modal Feature Sampling.

Mobile devices usually mount a depth sensor to resolve ill-posed problems, like salient object detection on cluttered background. The main barrier of exploring RGBD data is to handle the information from two different modalities. To cope with this problem, in this paper, we propose a boundary-aware cross-modal fusion network for RGBD salient object detection.

Learning Long-term Structural Dependencies for Video Salient Object Detection.

Existing video salient object detection (VSOD) methods focus on exploring either short-term or long-term temporal information. However, temporal information is exploited in a global frame-level or regular grid structure, neglecting interframe structural dependencies. In this paper, we propose to learn long-term structural dependencies with a structure-evolving graph convolutional network (GCN).

Transductive Zero-Shot Action Recognition via Visually Connected Graph Convolutional Networks.

With the explosive growth of action categories, zero-shot action recognition aims to extend a well-trained model to novel/unseen classes. To bridge the large knowledge gap between seen and unseen classes, in this brief, we visually associate unseen actions with seen categories in a visually connected graph, and the knowledge is then transferred from the visual features space to semantic space via the grouped attention graph convolutional networks (GAGCNs). In particular, we extract visual features for all the actions, and a visually connected graph is built to attach seen actions to visually similar unseen categories.

Video Snapshot: Single Image Motion Expansion via Invertible Motion Embedding.

Unlike images, finding the desired video content in a large pool of videos is not easy due to the time cost of loading and watching. Most video streaming and sharing services provide the video preview function for a better browsing experience. In this paper, we aim to generate a video preview from a single image.