In the literature, prior research on Security-oriented Video Understanding (SVU) has predominantly focused on detecting and locating the threats (e.g., shootings, robberies) in videos, while largely lacking the effective capability to generate and evaluate the threat causes. Motivated by these gaps, this paper introduces a new chat paradigm SVU task, i.e., In-depth Security-oriented Video Understanding (DeepSVU), which aims to not only identify and locate the threats but also attribute and evaluate the causes of threatening segments in detail. Furthermore, this paper reveals two key challenges in the proposed task: 1) how to effectively model the coarse-to-fine physical-world information (e.g., human behavior, object interactions and background context) to boost the DeepSVU task, and 2) how to adaptively trade off these factors. Addressing these challenges is crucial for improving VAD, especially for identifying, locating, and attributing anomalies. To tackle these challenges, this paper proposes a new Unified Physical-world Regularized MoE (UPRM) approach. Specifically, UPRM incorporates two key components: the Unified Physical-world Enhanced MoE (UPE) Block and the Physical-world Trade-off Regularizer (PTR), to address the above two challenges, respectively. Extensive experiments conduct on our DeepSVU instructions datasets (i.e., UCF-C instructions and CUVA instructions) demonstrate that UPRM outperforms several advanced Video-LLMs as well as non-LLM approaches. such information.These justify the importance of the coarse-to-fine physical-world information in the DeepSVU task and demonstrate the effectiveness of our UPRM in capturing such information.

Modern information retrieval (IR) must reconcile short, ambiguous queries with increasingly diverse and dynamic corpora. Query expansion (QE) remains a core technique for mitigating vocabulary mismatch, but its design space has been reshaped by pre-trained and large language models (PLMs/LLMs). This survey reviews QE methods in the PLM/LLM era and provides a unified view of the emerging landscape. We first summarize how different model families enable new expansion behaviors, including stronger contextualization, more controllable generation, and instruction-following. We then organize recent techniques along four complementary design dimensions: where expansion is injected in the pipeline, how it is grounded and interacts with corpus evidence, how it is learned or aligned, and how structured knowledge, such as knowledge graphs, is incorporated. Beyond taxonomy, we synthesize application patterns and deployment considerations across representative retrieval settings, highlighting practical tradeoffs among effectiveness, controllability, grounding quality, and operating cost. Finally, we outline open challenges and future directions toward more reliable, safe, efficient, and continually adaptive QE under real-world constraints (resources are available at https://github.com/lmh0921/QueryExpansion-PLM-LLM-Survey-paperList ).

Prior studies on Visual Sentiment Understanding (VSU) primarily rely on the explicit scene information (e.g., facial expression) to judge visual sentiments, which largely ignore implicit scene information (e.g., human action, objection relation and visual background), while such information is critical for precisely discovering visual sentiments. Motivated by this, this paper proposes a new Omni-scene driven visual Sentiment Identifying, Locating and Attributing in videos (Omni-SILA) task, aiming to interactively and precisely identify, locate and attribute visual sentiments through both explicit and implicit scene information. Furthermore, this paper believes that this Omni-SILA task faces two key challenges: modeling scene and highlighting implicit scene beyond explicit. To this end, this paper proposes an Implicit-enhanced Causal MoE (ICM) approach for addressing the Omni-SILA task. Specifically, a Scene-Balanced MoE (SBM) and an Implicit-Enhanced Causal (IEC) blocks are tailored to model scene information and highlight the implicit scene information beyond explicit, respectively. Extensive experimental results on our constructed explicit and implicit Omni-SILA datasets demonstrate the great advantage of the proposed ICM approach over advanced Video-LLMs.

Prior studies on Video Anomaly Detection (VAD) mainly focus on detecting whether each video frame is abnormal or not in the video, which largely ignore the structured video semantic information (i.e., what, when, and where does the abnormal event happen). With this in mind, this paper propose a new chat-paradigm Multi-scene Video Abnormal Event Extraction and Localization (M-VAE) task, aiming to extract the abnormal event quadruples (i.e., subject, event type, object, scene) and localize such event. Further, this paper believes that this new task faces two key challenges, i.e., global-local spatial modeling and global-local spatial balancing. To this end, this paper proposes a Global-local Spatial-sensitive Large Language Model (LLM) named Sherlock, i.e., acting like Sherlock Holmes to track down the criminal events, for this M-VAE task. Specifically, this model designs a Global-local Spatial-enhanced MoE (GSM) module and a Spatial Imbalance Regulator (SIR) to address the two challenges respectively. Extensive experiments on our M-VAE instruction dataset show the significant advantages of Sherlock over several advanced Video-LLMs. This justifies the importance of global-local spatial information for the M-VAE task and the effectiveness of Sherlock in capturing such information.

Cognitive reasoning holds a significant place within Natural Language Processing (NLP). Yet, the exploration of zero-shot scenarios, which align more closely with real-life situations than supervised scenarios, has been relatively limited. While a few studies have employed Large Language Models (LLMs) to tackle zero-shot cognitive reasoning tasks, they still grapple with two key challenges: 1) Traditional approaches rely on the chain-of-thought (CoT) mechanism, wherein LLMs are provided with a "Let's think step by step'' prompt. However, this schema may not accurately understand the meaning of a given question and ignores the possible learned knowledge (e.g., background or commonsense) of the LLMs about the questions, leading to incorrect answers. 2) Previous CoT methods normally exploit a single Large Language Model (LLM) and design many strategies to augment this LLM. We argue that the power of a single LLM is typically finite since it may not have learned some relevant knowledge about the question. To address these issues, we propose a Multi-LLM Knowledge Fusion (MLKF) approach, which resorts to heterogeneous knowledge emerging from multiple LLMs, for zero-shot cognitive reasoning tasks. Through extensive experiments and detailed analysis, we demonstrate that our MLKF can outperform the existing zero-shot or unsupervised state-of-the-art methods on four kinds of zero-shot tasks: aspect sentiment analysis, named entity recognition, question answering, and mathematical reasoning. Our code is available at https://github.com/trueBatty/MLKF

The growing importance of multi-modal humor detection within affective computing correlates with the expanding influence of short-form video sharing on social media platforms. In this paper, we propose a novel two-branch hierarchical model for short-form video humor detection (SVHD), named Comment-aided Video-Language Alignment (CVLA) via data-augmented multi-modal contrastive pre-training. Notably, our CVLA not only operates on raw signals across various modal channels but also yields an appropriate multi-modal representation by aligning the video and language components within a consistent semantic space. The experimental results on two humor detection datasets, including DY11k and UR-FUNNY, demonstrate that CVLA dramatically outperforms state-of-the-art and several competitive baseline approaches. Our dataset and code release at https://github.com/yliu-cs/CVLA.

This paper presents our pioneering effort in addressing a new and realistic scenario in multi-modal dialogue systems called Multi-modal Real-time Emotion Pre-recognition in Conversations (MREPC). The objective is to predict the emotion of a forthcoming target utterance that is highly likely to occur. We believe that this task can enhance the dialogue system's understanding of the interlocutor's state of mind, enabling it to prepare an appropriate response in advance. However, addressing MREPC poses the following challenges:1) Previous studies on emotion elicitation typically focus on textual modality and perform sentiment forecasting within a fixed contextual scenario. 2) Previous studies on multi-modal emotion recognition aim to predict the emotion of existing utterances, making it difficult to extend these approaches to MREPC due to the absence of the target utterance. To tackle these challenges, we construct two benchmark multi-modal datasets for MREPC and propose a task-specific multi-modal contrastive pre-training approach. This approach leverages large-scale unlabeled multi-modal dialogues to facilitate emotion pre-recognition for potential utterances of specific target speakers. Through detailed experiments and extensive analysis, we demonstrate that our proposed multi-modal contrastive pre-training architecture effectively enhances the performance of multi-modal real-time emotion pre-recognition in conversations.

Sentiment and emotion classification have been popularly but separately studied in natural language processing. In this paper, we address joint learning on sentiment and emotion classification where both the labeled data for sentiment and emotion classification are available. The objective of this joint-learning is to benefit the two tasks from each other for improving their performances. Specifically, an extra data set that is annotated with both sentiment and emotion labels are employed to estimate the transformation probability between the two kinds of labels. Furthermore, the transformation probability is leveraged to transfer the classification labels to benefit the two tasks from each other. Empirical studies demonstrate the effectiveness of our approach for the novel joint learning task.

Motivated by the critical importance of connectives in recognizing discourse relations, we present an unsupervised cross-argument inference mechanism to implicit discourse relation recognition. The basic idea is to infer the implicit discourse relation of an argument pair from a large number of comparable argument pairs, which are automatically retrieved from the web in an unsupervised way. In this way, the inference proceeds from explicit relations to implicit ones via connective as bridge. This kind of pair-to-pair inference is based on the assumption that two argument pairs with high content similarity (i.e. comparable argument pairs) should have similar discourse relationship. Evaluation on PDTB proves the effectiveness of our inference mechanism in implicit relation recognition to the four level-1 relations. It also shows that our mechanism significantly outperforms other alternatives.

Semi-supervised sentiment classification aims to train a classifier with a small number of labeled data (called seed data) and a large amount of unlabeled data. a big advantage of this approach is its saving of annotation effort by using the unlabeled data which is usually freely available. In this paper, we propose an approach to further minimize the annotation effort of semi-supervised sentiment classification by actively selecting the seed data. Specifically, a novel selection strategy is proposed to simultaneously select good words and documents for manual annotation by considering both of their annotation costs and informativeness. Experimental results demonstrate the effectiveness of our approach.

This paper focuses on exploring the features of product reviews that satisfy users, by which to improve the automatic helpfulness voting for the reviews on commercial websites. Compared to the previous work, which single-mindedly adopts the textual features to assess the review helpfulness, we propose that user preferences are more explicit clues to infer the opinions of users on the review helpfulness. By using the user-preference based features, we firstly implement a binary helpfulness based review classification system to divide helpful reviews and useless, and on the basis, we secondly build a Ranking SVM based automatic helpfulness voting system (AHV) which rank reviews based on their helpfulness. Experiments used a large scale dataset containing over 34,266 reviews on 1289 products to test the systems, which achieves promising performances with accuracy of up to 0.72 and [email protected] of 0.25, and at least 9% accuracy improvement compared to the textual-feature based helpfulness assessment.

Sentiment classification has undergone significant development in recent years. However, most existing studies assume the balance between negative and positive samples, which may not be true in reality. In this paper, we investigate imbalanced sentiment classification instead. In particular, a novel clustering-based stratified under-sampling framework and a centroid-directed smoothing strategy are proposed to address the imbalanced class and feature distribution problems respectively. Evaluation across different datasets shows the effectiveness of both the under-sampling framework and the smoothing strategy in handling the imbalanced problems in real sentiment classification applications.

This paper proposes a novel document re-ranking approach in information retrieval, which is done by a label propagation-based semi-supervised learning algorithm to utilize the intrinsic structure underlying in the large document data. Since no labeled relevant or irrelevant documents are generally available in IR, our approach tries to extract some pseudo labeled documents from the ranking list of the initial retrieval. For pseudo relevant documents, we determine a cluster of documents from the top ones via cluster validation-based k-means clustering; for pseudo irrelevant ones, we pick a set of documents from the bottom ones. Then the ranking of the documents can be conducted via label propagation. Evaluation on benchmark corpora shows that the approach can achieve significant improvement over standard baselines and performs better than other related approaches.