17 accepted papers
3D echocardiography provides superior cardiac quantification to traditional 2D echocardiography, which suffers from geometric idealizations and imaging plane misalignment. However, despite its advantages, clinical adoption of 3D echo remains limited due to logistical and visualization challenges. We
Accurate and efficient voxelized representations of 3D meshes are the foundation of 3D reconstruction and generation. However, existing representations based on iso-surface heavily rely on water-tightening or rendering optimization, which inevitably compromise geometric fidelity. We propose Faithful
In the domain of photorealistic talking head generation, the fidelity of audio-driven lip motion synthesis is essential for realistic virtual interactions. Existing methods face two key challenges: a lack of vivacity due to limited diversity in generated lip poses and noticeable anamorphose motions…
Meshes are the de facto 3D representation in the industry but are labor-intensive to produce. Recently, a line of research has focused on autoregressively generating meshes. This approach processes meshes into a sequence composed of vertices and then generates them vertex by vertex, similar to how a…
High-fidelity 3D object synthesis remains significantly more challenging than 2D image generation due to the unstructured nature of mesh data and the cubic complexity of dense volumetric grids. Existing two-stage pipelines—compressing meshes with a VAE (using either 2D or 3D supervision), followed b…
Adversarial robustness and interpretability are longstanding challenges of computer vision. Deep neural networks are vulnerable to adversarial perturbations that are incomprehensible and imperceptible to humans. However, the opaqueness of networks prevents one from theoretically addressing adversari…
This work proposes the quantum-state-based mapping (QSM) for machine learning. QSM uses wave functions that describe microscopic particle systems as mappings. By QSM, original inputs or features extracted by neural networks are processed as quantum states to train wave function parameters. QSM has a…
Realistic virtual humans play a crucial role in numerous industries, such as metaverse, intelligent healthcare, and self-driving simulation. But creating them on a large scale with high levels of realism remains a challenge. The utilization of deep implicit function sparks a new era of image-based 3…
Convolutional neural networks (CNNs) have achieved superior performance but still lack clarity about the nature and properties of feature extraction. In this paper, by analyzing the sensitivity of neural networks to frequencies and scales, we find that neural networks not only have low- and mediumfr…
Neuromorphic vision sensors (event cameras) are inherently suitable for spiking neural networks (SNNs) and provide novel neuromorphic vision data for this biomimetic model. Due to the spatiotemporal characteristics, novel data augmentations are required to process the unconventional visual signals o…
Neuromorphic vision sensors (event cameras) simulate biological visual perception systems and have the advantages of high temporal resolution, less data redundancy, low power consumption, and large dynamic range. Since both events and spikes are modeled from neural signals, event cameras are inheren…
As the third generation of neural networks, spiking neural networks (SNNs) are dedicated to exploring more insightful neural mechanisms to achieve near-biological intelligence. Intuitively, biomimetic mechanisms are crucial to understanding and improving SNNs. For example, the associative long-term…
It is a common paradigm in object detection frameworks to perform multi-scale detection. However, each scale is treated equally during training. In this paper, we carefully study the objective imbalance of multi-scale detector training. We argue that the loss in each scale is neither equally importa…
Existing super-resolution (SR) methods commonly assume that the degradation kernels are fixed and known (e.g., bicubic downsampling or single Gaussian blurring kernel). However, these methods suffer a severe performance drop when the real degradations deviate from this assumption. To address this is…
Feature Pyramid Network (FPN) has become a common detection paradigm by improving multi-scale features with strong semantics. However, most FPN-based methods typically treat each feature map equally and sum the loss without distinction, which might lead to suboptimal overall performance. In this pap…
Since a large proportion of real-world data is made of different representations or views, learning on data represented with multiple views (e.g., numerous types of features or modalities) has garnered considerable attention recently. Nonnegative matrix factorization (NMF) has been widely adopted fo…
As common cancer, breast cancer kills thousands of women every year. It’s significant to provide doctors computer-aided diagnosis (CAD) to ease their workload as well as improve detection quality. Patch-level CNNs are usually used to classify the breast tissue slice, and the CNNs classify each patch…