In this paper, we propose a Dual-Constraint Diffusion Model (DCDM) to contrast aging appearance for facial age estimation, addressing the key issue of noisy labels. Existing methods for face age estimation are plagued by class imbalance and noisy supervision signals, which disrupt the ordinal relationships between age categories and hinder effective feature decoupling in existing models. To overcome these challenges, the proposed DCDM develops a label-independent Paired Comparison, ensuring accurate sample labeling and maintaining continuity in age estimation. Moreover, we incorporate a Dual-Constraint Diffusion Model to effectively separate and recombine age-related and unrelated features, thus facilitating the generation of high-fidelity and continuous age-progressed facial representations. Lastly, we optimize our model parameters by exploiting the age difference information via an active learning framework. Comparative evaluations on several in-the-wild datasets demonstrate that our DCDM significantly achieves superior results compared to existing state-of-the-art methods in facial age estimation.

ABSTRACT Large‐scale multi‐objective optimization problems (LSMOPs) are characterised by concurrent optimization of multiple conflicting objectives and no fewer than 100 decision variables. They widely exist in the fields of practical engineering and scientific research. Over the past decade, many large‐scale multi‐objective evolutionary algorithms (LSMOEAs) have emerged to address LSMOPs. This paper systematically reviews and comprehensively analyzes the ideas, advantages, disadvantages, and latest developments of these LSMOEAs. Firstly, it introduces the relevant concepts of LSMOEAs. Then classify them into four categories: decision variable grouping‐based LSMOEAs, non‐grouping dimensionality reduction‐based LSMOEAs, effective offspring generation‐based LSMOEAs, and learning models‐based LSMOEAs. It analyzes representative algorithms in each category, elaborating on their core strategies, advantages, and disadvantages. Finally, it explores the applications of LSMOEAs in computer vision, like tackling pixel‐level correlation, high‐resolution feature redundancy, dynamic target tracking, and complex visual modelling. This paper provides readers with a comprehensive and systematic overview of LSMOEAs, serving as a valuable reference for both researchers entering this field and practitioners seeking to select appropriate algorithms for practical problems.

In this paper, we propose a probabilistic regression diffusion model for head pose estimation, dubbed HeadDiff, which typically addresses the rotation uncertainty, especially when faces are captured in wild conditions. Unlike conventional image-to-pose methods which cannot explicitly establish the rotational manifold of head poses, our HeadDiff aims to ensure the pose rotation via the diffusion process and in parallel, refine the mapping process iteratively. Specifically, we initially formulate the head pose estimation problem as a reverse diffusion process, defining a paradigm for progressive denoising on the manifold, which explores the uncertainty by decomposing the large gap into intermediate steps. Moreover, our HeadDiff is equipped with an isotropic Gaussian distribution by encoding the incoherence information in our rotation representation. Finally, we learn the facial relationship of nearest neighbors with a cycle-consistent constraint for robust pose estimation versus diverse shape variations. Experimental results on multiple datasets demonstrate that our proposed method outperforms existing state-of-the-art techniques without auxiliary data.

The search space of large-scale multi-objective optimization problems (LSMOPs) is huge because of the hundreds or even thousands of decision variables involved. It is very challenging to design efficient algorithms for LSMOPs to search the whole space effectively and balance the convergence and diversity at the same time. In this paper, to tackle this challenge, we develop a new algorithm based on a weighted optimization framework with two effective strategies. The weighted optimization framework transforms an LSMOP into multiple small-scale multi-objective optimization problems based on a problem transformation mechanism to reduce the dimensionality of the search space effectively. To further improve its effectiveness, we firstly propose a centerline symmetry strategy to select reference solutions to transform the LSMOPs. It takes not only some non-dominated solutions but also their centerline symmetric points as the reference solutions, which can enhance the population diversity to avoid the algorithm falling into local minima. Then, a new double-line transformation function is designed to expand the search range of the transformed problem to further improve the convergence and diversity. With the two strategies, more widely distributed potential search areas are provided and the optimal solutions can be found easier. To demonstrate the effectiveness of our proposed algorithm, numerical experiments on widely used benchmarks are executed and the statistical results show that our proposed algorithm is more competitive and performs better than the other state-of-the-art algorithms for solving LSMOPs.

Designing efficient algorithms for the large-scale multi-objective problems (LSMOPs) is a very challenging problem currently. To tackle LSMOPs, we first design a new reference points selection strategy to enhance the diversity of algorithms and avoid the algorithm falling into local minima. The strategy selects not only a part of nondominated solutions with the largest crowding distance, but also a part of relatively uniformly distributed solutions as the reference points. In this way, much better diversity and convergence can be obtained. Second, we propose a direction-guided offspring generation strategy, where a type of potential directions is designed to generate the promising solutions which can balance the convergence and diversity of the obtained solutions and improve the effectiveness of the algorithm significantly. Based on the two proposed strategies, we propose a new effective algorithm for LSMOPs. Numerical experiments are executed on two widely used large-scale multi-objective benchmark problem sets with 200, 500 and 1000 decision variables and a comparison with five state-of-the-art algorithms is made. The experimental results show that our proposed algorithm is effective and can obtain significantly better solutions than the compared algorithms.

In this paper, we propose a new method for large scale multi-objective optimization based on symmetric points search and variable grouping, named SSVG. The main idea is to use variable grouping scheme first to divide the original decision space into several subspaces. In each subspace, the symmetric points of the points in population form some potential search directions. Using the search directions, the possibility of finding the optimal solutions will increase greatly. Moreover, in order to decrease the dimension of problem, a new transformation function which transforms the decision space into a lower dimension search space (weight vector space) is designed. Furthermore, experiments are conducted on some benchmarks with 200, 500 and 1000 decision variables and the proposed algorithm SSVG is compared with three state-of-the-art algorithms: MOEA/DVA, WOF and LSMOF. The results show that the proposed algorithm outperforms the compared algorithms in term of convergence and diversity.