Current point cloud registration algorithms struggle to effectively handle both deformations and occlusions simultaneously. Our manifold analysis reveals this limitation arises from the inaccurate modeling of the shape's underlying manifold and the lack of an effective optimization strategy for fragmented manifold structures. In this paper, we present AniSym-Net, a novel non-rigid registration framework

Existing fusion methods empirically design elaborate fusion losses to retain the specific features from source images. Since image fusion has no ground truth, the hand-crafted losses may not make the fused images cover all the vital features, and then affect the performance of the high-level tasks. Here, there are two main challenges: domain discrepancy among source images and semantic mismatch at

Lifelong person re-identification (LReID) aims to learn from streaming data sources step by step, which suffers from the catastrophic forgetting problem. In this paper, we investigate the exemplar-free LReID setting where no previous exemplar is available during the new step training. Existing exemplar-free LReID methods primarily adopt knowledge distillation to transfer knowledge from an old model

Recent advances in self-supervised learning have witnessed great achievements, especially with the introduction of contrastive learning, where the goal is to maximize the mutual information between different augmentations of the same image, i.e., positive pairs. However, such optimization does not necessarily correspond to optimal representation due to noisy samples, thus inevitably being over-confident

Anomaly detection has garnered extensive applications in real industrial manufacturing due to its remarkable effectiveness and efficiency. However, previous generative-based models have been limited by suboptimal reconstruction quality, hampering their overall performance. We introduce DiffusionAD, a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network

As neural networks are trained to perform tasks of increasing complexity, their size increases, which presents several challenges in their deployment on devices with limited resources. To cope with this, a recently proposed approach hinges on substituting the classical Multiply-and-ACcumulate (MAC) neurons in the hidden layers with other neurons called Multiply-And-Max/min (MAM) whose selective behavior

Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks' representation learning. Existing GNNs often struggle to effectively leverage incomplete attribute information, as they are not specifically designed for graphs with missing attributes. To address this issue, we propose a novel node representation learning framework called Wasserstein

Due to the ill-posed nature of locating 3D objects based on image inputs, objects detected by camera-based detectors tend to have considerable uncertainty in their localization. Previous works in camera-based 3D detection and tracking represent each detected object as a single certain 3D bounding box, ignoring their localization uncertainty. We propose the uncertain representation of 3D objects to

Recent learning-based multi-view stereo (MVS) still exhibits insufficient accuracy in large occlusion cases, such as environments with significant inter-camera distance or when capturing objects with complex shapes. This is because incorrect image features extracted from occluded areas serve as significant noise in the cost volume construction. To address this, we propose a visibility-aware MVS using

Recent advancements in leveraging pre-trained 2D diffusion models achieve the generation of high-quality novel views from a single in-the-wild image. However, existing works face challenges in producing controllable novel views due to the lack of information from multiple views. In this paper, we present DreamComposer++, a flexible and scalable framework designed to improve current view-aware diffusion

Recent Mixup-based data augmentation methods have integrated saliency information for richer supervisory signals. However, they often face significant computational burdens, require additional modules, or are constrained by specific architectures. To overcome these limitations, we present GuidedMixup, a model-agnostic, saliency-aware mixup strategy. Unlike previous methods that struggle with random