Large language models (LLMs) generate fluent, context-rich text but suffer from hallucinations and limited interpretability. We introduce SenticNet 9, a neurosymbolic framework that automates commonsense reasoning while preserving transparency. It leverages conceptual primitive discovery (CPD) to learn foundational concepts and a time shift mechanism (TSM) to iteratively refine them through temporal feedback. This combination yields a scalable, cognitively inspired architecture that merges symbolic interpretability with LLM generalization. Experiments show SenticNet 9 outperforming embeddings, transformers, and state-of-the-art LLMs across tasks, delivering higher accuracy without sacrificing explainability.

Aspect-based summarization aims to generate summaries tailored to specific aspects, addressing the resource constraints and limited generalizability of traditional summarization approaches. Recently, large language models have shown promise in this task without the need for training. However, they rely excessively on prompt engineering and face token limits and hallucination challenges, especially with in-context learning. To address these challenges, in this paper, we propose a novel framework for aspect-based summarization: Self-Aspect Retrieval Enhanced Summary Generation. Rather than relying solely on in-context learning, given an aspect, we employ an embedding-driven retrieval mechanism to identify its relevant text segments. This approach extracts the pertinent content while avoiding unnecessary details, thereby mitigating the challenge of token limits. Moreover, our framework optimizes token usage by deleting unrelated parts of the text and ensuring that the model generates output strictly based on the given aspect. With extensive experiments on benchmark datasets, we demonstrate that our framework not only achieves superior performance but also effectively mitigates the token limitation problem.

Preference-based Reinforcement Learning (PBRL) relies on the efficient collection and use of preference data to train accurate reward functions, enabling agents to learn directly from human preferences. This process allows agents to better understand human intentions while effectively reducing biases inherent in AI systems. The pairwise comparison method gathers diverse preference data, and Seqrank expands preference datasets through transitivity, both fail to establish preference relationships across different rounds of labeling. This limitation can result in fragmented signals and slow convergence toward the optimal policy. To address this, we propose the Global Tree (GTree), a method built on the Seqrank framework that integrates trajectory preferences across multiple rounds, providing a unified representation of global preferences. Moreover, we posit that different trajectory comparison methods offer distinct advantages depending on the task and the stage of training. To fully exploit these strengths, we introduce FLIP. This adaptive strategy dynamically selects either the pairwise method or GTree based on historical performance, optimizing method use for each task and training stage. Our evaluations demonstrate that integrating cross-round preferences accelerates the convergence of the reward function, while the FLIP strategy further enhances learning efficiency and overall performance, thereby enabling agents to better understand human intentions.

Multimodal aspect-based sentiment classification (MASC) is an emerging task due to an increase in user-generated multimodal content on social platforms, aimed at predicting sentiment polarity toward specific aspect targets (i.e., entities or attributes explicitly mentioned in text-image pairs). Despite extensive efforts and significant achievements in existing MASC, substantial gaps remain in understanding fine-grained visual content and the cognitive rationales derived from semantic content and impressions (cognitive interpretations of emotions evoked by image content). In this study, we present Chimera: acognitive and aesthetic sentiment causality understanding framework to derive fine-grained holistic features of aspects and infer the fundamental drivers of sentiment expression from both semantic perspectives and affective-cognitive resonance (the synergistic effect between emotional responses and cognitive interpretations). The framework aligns visual patches with words, extracts coarse and fine-grained visual features, translates them into textual descriptions, and uses LLM-generated sentimental causes and impressions to boost sensitivity to affective cues. Experiments on MASC datasets show the model's effectiveness and greater flexibility compared to LLMs like GPT-4o. We have publicly released the complete implementation and dataset athttps://github.com/Xillv/Chimera

To achieve outstanding aspect-level sentiment analysis (ASC), it is crucial to reduce the distance between aspect terms and opinion words. Recently, advanced methods in ASC used graph neural network (GNN)-based methods to leverage the syntactic dependency within the sentence, which can shorten the distance through syntactical dependencies. However, existing approaches that utilize GNNs have difficulty extracting long-distance relations in the dependency tree due to the over-smoothing problem resulting from stacking GNN layers, which limits their ability to detect remote relations. To solve this issue, we propose a Bidirectional Directed Acyclic Graph (BDAG) to reconstruct syntactic dependencies and a Bidirectional Directed Acyclic Graph Neural Network (BDAGNN) to efficiently propagate multi-hop sentiment information. We also enhance the BDAG with affective commonsense knowledge from SenticNet for comprehensive sentiment classification. The BDAGNN we proposed obtains partial state-of-the-art performance on four benchmark datasets, indicating the feasibility of encoding syntactic structures with BDAG.

The field of image generation models has seen substantial progress, characterized by a proliferation of diverse generative models and their associated outputs. However, there currently exists a deficiency in methodologies that can concurrently and effectively evaluate both the intrinsic quality of generated images and the alignment between image features and textual prompts. To address these challenges, we propose a novel Framework for evaluating Visual elegance and Sentiment resonance (FVS). The FVS incorporates a novel image aesthetic assessment model, specifically trained to assess the visual attractiveness of the generated images. Additionally, it evaluates the sentiment and aesthetic consistency between textual prompt and the generated image. Experimental results verify that the evaluations from our framework align more closely with human preferences. Moreover, we apply our framework to filter and construct a higher-quality training set of generated images. This curated dataset is then exploited to adapt the generative model, resulting in enhanced generation quality.

Few-shot segmentation has attracted growing interest owing to its value in practical applications. The primary challenge of few-shot segmentation lies in semantic information discovery, especially for query images. To tackle this issue, we propose a dual-expert distillation network (DEDN) made up of a scenario-level expert and an object-level expert to obtain semantic information from different perspectives. In DEDN, experts can learn from each other through online knowledge distillation with positive-guided Kullback-Leibler divergence. We innovate the Scenario Normalization and Object Continuity Guidance on dual experts to guarantee the various perspectives respectively. We further propose the Adaptive Weighted Fusion to adapt the trained experts to novel classes and obtain reliable fused predictions. Extensive experiments on Pascal-5i and COCO-20i show that our approach achieves state-of-the-art results.

Nowadays, with the explosive growth of multimodal reviews on social media platforms, multimodal sentiment analysis has recently gained popularity because of its high relevance to these social media posts. Although most previous studies design various fusion frameworks for learning an interactive representation of multiple modalities, they fail to incorporate sentimental knowledge into inter-modality learning. This pa-per proposes a Multi-channel Attentive Graph Convolutional Network (MAGCN), consisting of two main components: cross-modality interactive learning and sentimental feature fusion. For cross-modality interactive learning, we exploit the self-attention mechanism combined with densely connected graph convolutional networks to learn inter-modality dynamics. For sentimental feature fusion, we utilize multi-head self-attention to merge sentimental knowledge into inter-modality feature representations. Extensive experiments are conducted on three widely-used datasets. The experimental results demonstrate that the proposed model achieves competitive performance on accuracy and F1 scores compared to several state-of-the-art approaches.

Document layout analysis plays a vital role in computer vision research. Current document layout analysis methods mostly use pixel-based classification for document layout analysis. However, the method based on pixel classification is insufficient for maintaining the continuity of the classification area. In this paper, we propose a document layout analysis method based on positional encoding and bounding box specification. We maintain the continuity of the analysis area by constructing a document layout analysis framework based on the bounding box. In addition, we also integrate a positional encoding module in the framework to maintain the detailed information in the document layout analysis and modeling process. Experimental results prove that our proposed method has achieved state-of-the-art results.

Anomaly localization is pixel-level regions detection in the image. The challenge is how to generate accurate representations of the novel anomaly types which are multifarious. Besides, the anomaly sample size is often not enough to support model learning to detection because of the limitations of real conditions. In this work, we present a novel few-shot setting for anomaly detection and reorganize the defective datasets. Based on the few-shot learning, we transfer the idea of metric learning and propose the prototype-guided transfer network (PGTNet). Extensive experiment results suggest that PGT-Net outperforms current SOTA methods and provides a novel perspective for the anomaly localization task.

The multi-modal target-oriented sentiment analysis aims at predicting the sentiment polarities for target entities in a sentence by combining vision and language information. However, most existing deep learning approaches fail to extract valuable information from the visual modality and ignore the usability of syntactic dependency information embedded in the text modality. In this paper, we propose a two-stream adaptive multi-feature extraction graph convolutional networks (AME-GCN), which translates the image into a textual caption and dynamically fuses the semantic and syntactic feature from the given sentence and generated caption to model the inter/intra-modality dynamics. Extensive experiments on two multi-modal Twitter datasets show the effectiveness of the proposed model against popular textual and multi-modal approaches, demonstrating that AME-GCN is a best alternative for this task.

Depth completion technology based on multi-level feature fusion (MF) strategy has recently achieved remarkable success. However, the existing MF-based methods treat RGB features and depth map features equally when performing modal fusion but ignore the difference in semantic richness and sparsity between them, which leads to the results generated by these methods overfitting the shape of RGB and harm to the accuracy of depth value. To address this problem, we proposed a novel dual fusion (DF) strategy for MF-based depth completion, which can prevent overfitting by weakening the influence of RGB features on the generated results through two fusion stages. The entire DF framework consists of two multi-level fusion modules. The first fusion module performs a simple fusion of RGB features and depth features, while the second fusion module enriches the sparse image representation with the previously obtained fused features. Besides, we utilize non-local sparse attention to solve the problem that ordinary convolution is not capable of expressing depth map features enough. We test our approach on the outdoor KITTI test set and achieve the state-of-the-art (SOTA) performance in RMSE. Extensive experiments on the indoor NYUv2 dataset and KITTI validation set further demonstrate that our approach outperforms existing MF-based methods.

Aspect-level sentiment classification (ASC), detecting and predicting the sentiment polarity of the given aspecs, has attracted increasing attention in the field of Natural Language Processing (NLP). Recent studies in ASC leveraged the graph based on the dependency tree of the context to incorporate the syntactic information and structure of a sentence for better relation extraction. Some researchers noted that existing methods ignored semantic relations or failed to consider affective dependency information, and then proposed several state-of-art methods tackling the above two limitations. However, these approaches failed to consider both informative relations simultaneously. Therefore, we explore and propose a novel solution based on semantic latent graph and SenticNet to leverage semantic and affective information. Specifically, we build a latent semantic graph based on self-attention networks to parse semantic relations within the contexts. In addition, we utilize affective knowledge from SenticNet to enhance the dependency graphs of sentences. Moreover, we use the gate mechanism to dynamically combine information from both the enhanced dependency graphs and latent semantic graphs. Experimental results on three benchmark datasets illustrate the effectiveness and state-of-the-art performance of our model.

Aspect-level sentiment analysis aims to predict the sentiment polarity toward a specific aspect in a sentence. Most current approaches are based on deep learning and the attention mechanism. However, these models cannot simultaneously include the context semantic information carried by local words and the global syntactic information possibly carried by remote words. In this paper, we propose a local semantic and global syntactic integration scheme, which employs a local focus mechanism over local context words and exploits improved graph convolutional networks over dependency tree to encode global syntactic information. Moreover, multi-head attention is used to capture both the semantic information and also the interactive information between semantics and syntactic features. Experimental results on five datasets show the effectiveness of our model over a series of latest models.

Aspect-based sentiment analIsis (ABSA) aims to detect the sentiment polaritI of a specific aspect in an opinionated sentence. Current work focuses on exploiting the sIntactic tree to shorten the distance between the aspect term and context words. However, the “hard-pruning” strategI on the sIntactic tree maI lead to the reduction of importa nt sIntactic information. In this paper, we propose a novel sInt actical distance attention guided graph convolutional network (SDGCN) for ABSA. Our model is capable of fullI exploiting the sIntactic knowledge with a “soft pruning” strategI and learning crucial fine-grain sIntactic distance info rmation. AdditionallI, an effective denselI connected graph convolutional laIer is applied to avoid the over-sm oothing problem of standard GCN. Experiments conducted on three benchmark datasets show that our model achieves promising results comparing to the baseline models.