DCompared to large language models, traditional multimodal sentiment analysis frameworks are constrained by their classification heads, resulting in poor performance on zeroshot tasks. Moreover, due to limitations in visual encoders and multimodal fusion modules, most existing frameworks can only process a small number of images, leading to a loss of visual information. In light of these issues, this paper proposes a new framework, MSACC. This framework enhances the model's zero-shot performance by adopting a contrastive classification method and reduces the loss of visual information through visual relation extraction and three-dimensional sentiment analysis. We conducted extensive experiments on the Yelp dataset. The experimental results show that MSACC outperforms models of the same category in zero-shot MSA tasks, achieving a 48% performance improvement. Furthermore, compared to the large language model ChatGLM2-6B, MSACC still achieved a 7% performance increase while saving 90% of the model size. In addition, in supervised tasks, MSACC also achieved a 3.27% performance improvement compared to the baseline model.
The task of knowledge graph entity typing (KGET) aims to infer the missing types for entities in knowledge graphs, which is a significant subtask of knowledge graph completion (KGC). In despite of its progress, we observe that the sparsity of the dataset greatly affects the task itself as well as downstream tasks. In this paper, we propose a framework to alleviate this problem, which consists of data augmentation and type inference. We introduce a Statistics-based Entity Type Data Augmentation (SET-DA) method in data augmentation phase, which calculates a type probability distribution for each entity by statistically determining the global relation-type statistics information, and then employ embedding-based KGET models for type inference. Experimental results on two widely used datasets indicate that the proposed framework can solve the data sparsity problem in the task of KGET and the performance of models trained with data augmentation by SET-DA significantly outperforms previous state-of-the-art methods.
Optical image dehazing is a challenging task. Although both physical model-based and deep learning-based dehazing methods have achieved a certain degree of restoration results, they are all reflected in a single spatial domain processing, and deep learning methods have the problem of large models. We start from the flow of information, aiming to combine pixel and frequency domain information, redesign the up- and downsampling mechanism based on discrete wavelet transform, and separate the frequency spectrum of features in the neck network for auxiliary processing, making full use of frequency domain information to assist the dehazing process. Considering the basic nature of the dehazing task in image processing, we introduce feature fusion guided by brightness and texture information to restore higher quality clear and haze-free images. We conducted experiments on SOTS-Indoor, NH-haze, and Dense-haze datasets. The results show that on the Indoor dataset, as shown in Fig.1, Our model parameters and computational costs are reduced by at least 60%, achieving a 41.11dB effect with only 16.48GFlops of computational cost and 1.89M parameters.
As the Internet's complexity and information volume surge, the need for efficient web automation becomes critical. Traditional web agents struggle with redundant web content, which disrupts their understanding of the environment. They also face inefficiencies in multi-task scenarios due to handcrafted exemplars and encounter error accumulation in long-horizon tasks, exacerbated by web-specific complexities like nested structures and interactive elements. To address these issues, we introduce WebSurfer, a novel web agent designed to filter, learn, and adapt in complex environments. WebSurfer refines task-oriented states for clearer observations and employs an exemplar retrieval and ordering strategy to enhance LLMs' understanding and adaptability to current tasks. Notably, WebSurfer features a novel web-wise insight feedback mechanism that enables continuous adaptation and strategy refinement. Evaluations demonstrate that WebSurfer outperforms state-of-the-art (SOTA) methods on realistic tasks, achieving higher accuracy and enhancing longterm adaptability.
Many signal processing and machine learning applications are framed as constrained optimization problems with positive definite constraints. Important examples include kernel matrix learning, covariance estimation of Gaussian distributions, maximum likelihood parameter estimation of elliptically contoured distributions, parameter estimation in Gaussian mixture models and matrix square root. In this work, utilizing Riemannian geometric principles, we propose coordinate-descent algorithms on the Riemannian manifold of symmetric positive definite (SPD) matrices. We further identify a broad class of functions encompassing all the aforementioned applications. Finally, we demonstrate that for this class of functions, the proposed algorithm achieves per-iteration complexities of O(n)-an order of magnitude lower than the O(n(3)) or higher complexities of full update algorithms like Riemannian gradient-descent and interior-point methods. Interestingly, the proposed algorithm is significantly faster than the Burer-Monteiro factorizationbased coordinate descent algorithm, which has a per-iteration complexity of O(n(2)), as confirmed through simulations on the log-det minimization problem.
