Object description plays an important role for visually impaired individuals to understand and compare the differences between objects. Recent multimodal large language models (MLLMs) exhibit powerful perceptual abilities and demonstrate impressive potential for generating object-centric descriptions. However, the descriptions generated by such models may still usually contain a lot of content that is not relevant to the user intent or miss some important object dimension details. Under special scenarios, users may only need the details of certain dimensions of an object. In this paper, we propose a training-free object description refinement pipeline,Dimension Tailor, designed to enhance user-specified details in object descriptions. This pipeline includes three steps: dimension extracting, erasing, and supplementing, which decompose the description into user-specified dimensions. Dimension Tailor can not only improve the quality of object details but also offer flexibility in including or excluding specific dimensions based on user preferences. We conducted extensive experiments to demonstrate the effectiveness of Dimension Tailor on controllable object descriptions. Notably, the proposed pipeline can consistently improve the performance of the recent MLLMs. The code is currently accessible athttps://github.com/PRIS-CV/ControllableObjectDescription.

Abstract. This paper is concerned with the problem of inverse scattering of time-harmonic acoustic plane waves by a two-layered medium with a locally rough interface in two dimensions. A direct imaging method is proposed to reconstruct the locally rough interface from the phaseless total-field data measured on the upper half of the circle with a large radius at a fixed frequency or from the phased far-field data measured on the upper half of the unit circle at a fixed frequency. The presence of the locally rough interface poses challenges in the theoretical analysis of the imaging methods. To address these challenges, a technically involved asymptotic analysis is provided for the relevant oscillatory integrals involved in the imaging methods, based mainly on the techniques and results in our recent work [L. Li, J. Yang, B. Zhang, and H. Zhang, arXiv:2208.00456 , 2022] on the uniform far-field asymptotics of the scattered field for acoustic scattering in a two-layered medium. Finally, extensive numerical experiments are conducted to demonstrate the feasibility and robustness of our imaging algorithms.

Observing objects' attributes at different levels of detail is a fundamental aspect of how humans perceive and understand the world around them. Existing studies focused on attribute prediction in a flat way, but they overlook the underlying attribute hierarchy, e.g., navy blue is a subcategory of blue. In recent years, large language models, e.g., ChatGPT, have emerged with the ability to perform an extensive range of natural language processing tasks like text generation and classification. The factual knowledge learned by LLM can assist us build the hierarchical relations of visual attributes in the wild. Based on that, we propose a model called the object-specific attribute relation net, which takes advantage of three types of relations among attributes - positive, negative, and hierarchical - to better facilitate attribute recognition in images. Guided by the extracted hierarchical relations, our model can predict attributes from coarse to fine. Additionally, we introduce several evaluation metrics for attribute hierarchy to comprehensively assess the model's ability to comprehend hierarchical relations. Our extensive experiments demonstrate that our proposed hierarchical annotation brings improvements to the model's understanding of hierarchical relations of attributes, and the object-specific attribute relation net can recognize visual attributes more accurately.

In this paper we study the inverse scattering problem of identifying a two-layered cavity by internal acoustic measurements. A uniqueness result is first provided for the determination of the interior interface by measurements corresponding to many incident point sources. Then the factorization method is proved to be valid in reconstructing the interior interface by using the same measurements. Finally, we carry out several numerical examples to illustrate the practicability of the inversion algorithm.

This paper is concerned with inverse scattering of plane waves by a locally perturbed infinite plane (which is called a locally rough surface) with the modulus of the total-field data (also called the phaseless near-field data) at a fixed frequency in two dimensions. We consider the case where a Dirichlet boundary condition is imposed on the locally rough surface. This problem models inverse scattering of plane acoustic waves by a one-dimensional sound-soft, locally rough surface; it also models inverse scattering of plane electromagnetic waves by a locally perturbed, perfectly reflecting, infinite plane in the transverse electric polarization case. We prove that the locally rough surface is uniquely determined by the phaseless near-field data generated by a countably infinite number of plane waves and measured on an open domain above the locally rough surface. Further, a direct imaging method is proposed to reconstruct the locally rough surface from the phaseless near-field data generated by plane waves and measured on the upper part of the circle with a sufficiently large radius. Theoretical analysis of the imaging algorithm is derived by making use of properties of the scattering solution and results from the theory of oscillatory integrals (especially the method of stationary phase). Moreover, as a by-product of the theoretical analysis, a similar direct imaging method with full far-field data is also proposed to reconstruct the locally rough surface. Finally, numerical experiments are carried out to demonstrate that the imaging algorithms with phaseless near-field data and full far-field data are fast, accurate, and very robust with respect to noise in the data.

This paper is concerned with the inverse scattering problem by an unbounded rough surface. A direct imaging method is proposed to reconstruct the rough surface from the scattered near-field Cauchy data generated by point sources and measured on a horizontal straight line segment at a finite distance above the rough surface. Theoretical analysis of the imaging algorithm is given for the case of a penetrable rough surface, but the imaging algorithm also works for impenetrable surfaces with Dirichlet or impedance boundary conditions. Numerical experiments are presented to show that the direct imaging algorithm is fast, accurate, and very robust with respect to noise in the data.