Continual learning (CL) has been a critical topic in contemporary deep neural network applications, where higher levels of both forward and backward transfer are desirable for an effective CL performance. Existing CL strategies primarily focus on task models — either by regularizing model updates or by separating task-specific and shared components — while often overlooking the potential of leveraging inter-task relationships to enhance transfer. To address this gap, we propose a transferability-aware task embedding, termed H-embedding, and construct a hypernet framework under its guidance to learn task-conditioned model weights for CL tasks. Specifically, H-embedding is derived from an information theoretic measure of transferability and is designed to be online and easy to compute. Our method is also characterized by notable practicality, requiring only the storage of a low-dimensional task embedding per task and supporting efficient end-to-end training. Extensive evaluations on benchmarks including CIFAR-100, ImageNet-R, and DomainNet show that our framework performs prominently compared to various baseline and SOTA approaches, demonstrating strong potential in capturing and utilizing intrinsic task relationships. Our code is publicly available at \url{https://github.com/viki760/Hembedding_Guided_Hypernet}.

Multi-source transfer learning is an effective solution to data scarcity by utilizing multiple source tasks for the learning of the target task. However, access to source data and model details is limited in the era of commercial models, giving rise to the setting of multi-source-free (MSF) transfer learning that aims to leverage source domain knowledge without such access. As a newly defined problem paradigm, MSF transfer learning remains largely underexplored and not clearly formulated. In this work, we adopt an information theoretic perspective on it and propose a framework named H-ensemble, which dynamically learns the optimal linear combination, or ensemble, of source models for the target task, using a generalization of maximal correlation regression. The ensemble weights are optimized by maximizing an information theoretic metric for transferability. Compared to previous works, H-ensemble is characterized by: 1) its adaptability to a novel and realistic MSF setting for few-shot target tasks, 2) theoretical reliability, 3) a lightweight structure easy to interpret and adapt. Our method is empirically validated by ablation studies, along with extensive comparative analysis with other task ensemble and transfer learning methods. We show that the H-ensemble can successfully learn the optimal task ensemble, as well as outperform prior arts.

Deep hashing has great potential in large-scale visual similarity search due to its preferable efficiency in storage and computation. Technically, deep hashing for visual similarity search inherits the powerful representation capability of deep neural networks, and it encodes visual features into compact binary codes by preserving representative semantic visual features. Works in this field mainly focus on building the relationship between the visual and objective hash spaces, while they seldom study the triadic cross-domain semantic knowledge transfer among visual, semantic, and hashing spaces, leading to a serious semantic ignorance problem during space transformation. In this article, we propose a novel deep tripartite semantically interactive hashing framework, dubbed Semantically Cycle-consistent Hashing Networks (SCHNs), for discriminative hash code learning. Particularly, we construct a flexible semantic space and a transitive latent space, in conjunction with the visual space, to jointly deduce the privileged discriminative hash space. Specifically, a new semantic space is conceived to strengthen the flexibility and completeness of categories in the semantic feature inference phase. At the same time, a transitive latent space is formulated to explore and uncover the shared semantic interactivity embedded in visual and semantic features. Moreover, to further ensure semantic consistency across multiple spaces, we propose to build a cyclic adversarial learning module to preserve and keep their semantic concurrence during space transformation. Notably, our SCHN, for the first time, establishes the cyclic principle of deep semantic-preserving hashing by adaptive semantic parsing across different spaces in a single-modal visual similarity search. In addition, the entire learning framework is jointly optimized in an end-to-end manner. Extensive experiments performed on diverse large-scale datasets evidence the superiority of our method against other state-of-the-art deep hashing algorithms. The source codes of this article are available at https://github.com/JalinWang/SCHN.

Deep hashing has shown great potentials in large-scale visual similarity search due to preferable storage and computation efficiency. Typically, deep hashing encodes visual features into compact binary codes by preserving representative semantic visual features. Works in this area mainly focus on building the relationship between the visual and objective hash space, while they seldom study the triadic cross-domain semantic knowledge transfer among visual, semantic and hashing spaces, leading to serious semantic ignorance problem during space transformation. In this paper, we propose a novel deep tripartite semantically interactive hashing framework, dubbed Semantically Cycle-consistent Hashing Networks (SCHN), for discriminative hash code learning. Particularly, we construct a flexible semantic space and a transitive latent space, in conjunction with the visual space, to jointly deduce the privileged discriminative hash space. Specifically, a semantic space is conceived to strengthen the flexibility and completeness of categories in feature inference. Moreover, a transitive latent space is formulated to explore the shared semantic interactivity embedded in visual and semantic features. Our SCHN, for the first time, establishes the cyclic principle of deep semantic-preserving hashing by adaptive semantic parsing across different spaces in visual similarity search. In addition, the entire learning framework is jointly optimized in an end-to-end manner. Extensive experiments performed on diverse large-scale datasets evidence the superiority of our method against other state-of-the-art deep hashing algorithms.

We introduce the notion of oriented charges to compute distance fields from unorganized point clouds. Unlike traditional implicit function methods that usually require normal information, our approach only uses information about the position of the samples. Our approach is adaptive, using an octree to reorganize the input points, thus avoiding difficulties often associated with the use of local distance fields. The resulting representation is simple, efficient and general in the sense that it handles complex models of arbitrary topology, is robust in the presence of noise and fills holes automatically.

This paper presents a novel approach for surface reconstruction from point clouds. The proposed technique is general in the sense that it naturally handles both manifold and non-manifold surfaces, providing a consistent way for reconstructing closed surfaces as well as surfaces with boundaries. It is also robust in the presence of noise, irregular sampling and surface gaps. Furthermore, it is fast, parallelizable and easy to implement because it is based on simple local operations. In this approach, surface reconstruction consists of three major steps: first, the space containing the point cloud is subdivided, creating a voxel representation. Then, a voxel surface is computed using gap filling and topological thinning operations. Finally, the resulting voxel surface is converted into a polygonal mesh. We demonstrate the effectiveness of our approach by reconstructing polygonal models from range scans of real objects as well as from synthetic data.

We propose a simple, novel and yet effective method for building and testing decision trees that minimizes the sum of the misclassification and test costs. More specifically, we first put forward an original and simple splitting criterion for attribute selection in tree building. Our tree-building algorithm has many desirable properties for a cost-sensitive learning system that must account for both types of costs. Then, assuming that the test cases may have a large number of missing values, we design several intelligent test strategies that can suggest ways of obtaining the missing values at a cost in order to minimize the total cost. We experimentally compare these strategies and C4.5, and demonstrate that our new algorithms significantly outperform C4.5 and its variations. In addition, our algorithm's complexity is similar to that of C4.5, and is much lower than that of previous work. Our work is useful for many diagnostic tasks which must factor in the misclassification and test costs for obtaining missing information.

We present a system for simulating and visualizing the propagation of dispersive contaminants with an application to urban security. In particular, we simulate airborne contaminant propagation in open environments characterised by sky-scrapers and deep urban canyons. Our approach is based on the multiple relaxation time lattice Boltzmann model (MRTLBM), which can efficiently handle complex boundary conditions such as buildings. In addition, we model thermal effects on the flow field using the hybrid thermal MRTLBM. Our approach can also accommodate readings from various sensors distributed in the environment and adapt the simulation accordingly. We accelerate the computation and efficiently render many buildings with small textures on the GPU. We render streamlines and the contaminant smoke with self-shadowing composited with the textured buildings.

This paper addresses the problem of surface reconstruction of highly noisy point clouds. The surfaces to be reconstructed are assumed to be 2-manifolds of piecewise C/sup 1/ continuity, with isolated small irregular regions of high curvature, sophisticated local topology or abrupt burst of noise. At each sample point, a quadric field is locally fitted via a modified moving least squares method. These locally fitted quadric fields are then blended together to produce a pseudo-signed distance field using Shepard's method. We introduce a prioritized front growing scheme in the process of local quadrics fitting. Flatter surface areas tend to grow faster. The already fitted regions will subsequently guide the fitting of those irregular regions in their neighborhood.

The modeling of real scenes is a complex and challenging task for which the use of laser rangefinders is one of the most promising approaches. Unfortunately, in many situations, it is not possible or practical to guarantee appropriate sampling of all surfaces in the scene. For example, occlusions and accessibility limitations to certain regions of the scene may cause some areas not to be visible by the scanner, resulting in incomplete or incorrectly reconstructed models. This paper describes a pipeline and a system implementation for improving model reconstruction from incomplete information available from range images. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism, Virtual Reality