Multimodal Large Language Model (MLLM) Personalization is a critical research problem that facilitates personalized dialogues with MLLMs targeting specific entities (known as personalized concepts). However, existing methods and benchmarks focus on the simple, context-agnostic visual identification and textual replacement of the personalized concept (e.g., "A yellow puppy" -> "Your puppy Mochi"), overlooking the ability to support long-context conversations. An ideal personalized MLLM assistant is capable of engaging in long-context dialogues with humans and continually improving its experience quality by learning from past dialogue histories. To bridge this gap, we propose LCMP, the first Long-Context MLLM Personalization evaluation benchmark. LCMP assesses the capability of MLLMs in perceiving variations of personalized concepts and generating contextually appropriate personalized responses that reflect these variations. As a strong baseline for LCMP, we introduce a novel training-free and state-aware framework TAME. TAME endows MLLMs with double memories to manage the temporal and persistent variations of each personalized concept in a differentiated manner. In addition, TAME incorporates a new training-free Retrieve-then-Align Augmented Generation (RA2G) paradigm. RA2G introduces an alignment step to extract the contextually fitted information from the multi-memory retrieved knowledge to the current questions, enabling better interactions for complex real-world user queries. Experiments on LCMP demonstrate that TAME achieves the best performance, showcasing remarkable and evolving interaction experiences in long-context scenarios.

The proliferation of harmful memes on online media poses significant risks to public health and stability. Existing detection methods heavily rely on large-scale labeled data for training, which necessitates substantial manual annotation efforts and limits their adaptability to the continually evolving nature of harmful content. To address these challenges, we present ALARM, the first lAbeL-free hARmful Meme detection framework powered by Large Multimodal Model (LMM) agent self-improvement. The core innovation of ALARM lies in exploiting the expressive information from "shallow" memes to iteratively enhance its ability to tackle more complex and subtle ones. ALARM consists of a novel Confidence-based Explicit Meme Identification mechanism that isolates the explicit memes from the original dataset and assigns them pseudo-labels. Besides, a new Pairwise Learning Guided Agent Self-Improvement paradigm is introduced, where the explicit memes are reorganized into contrastive pairs (positive vs. negative) to refine a learner LMM agent. This agent autonomously derives high-level detection cues from these pairs, which in turn empower the agent itself to handle complex and challenging memes effectively. Experiments on three diverse datasets demonstrate the superior performance and strong adaptability of ALARM to newly evolved memes. Notably, our method even outperforms label-driven methods. These results highlight the potential of label-free frameworks as a scalable and promising solution for adapting to novel forms and topics of harmful memes in dynamic online environments.

Micro-video popularity prediction (MVPP) plays a crucial role in numerous real-world applications, including product marketing and recommendation systems. While existing methodologies predominantly assume complete modalities during multimodal learning, this assumption often fails to hold in practical scenarios due to various constraints, such as privacy concerns or data integrity issues. To address this limitation, we propose SCRAG, a novel Self-Correlation Retrieval-Augmented Generative framework designed to enhance missing-modality robustness in MVPP. SCRAG operates in a retrieval-guided generation manner that explores relevant knowledge to enhance the reconstruction of missing content, which consists of two primary components: (1) a self-correlation retriever and (2) a multimodal mixture-of-experts generator. It first acquires instances pertinent to the missing content through multimodal prompt alignment. Subsequently, the generator extracts contextual modal information from the retrieved context-rich instances. By learning the joint distribution of modalities, SCRAG effectively recovers missing content and addresses the modal heterogeneity challenge inherent in cross-modal generation approaches. Extensive experiments conducted on three real-world datasets demonstrate that SCRAG consistently outperforms state-of-the-art baselines, underscoring its effectiveness in handling incomplete modalities and improving the accuracy of micro-video popularity prediction.

Zero-shot composed image retrieval (ZS-CIR) is a challenging task that aims to retrieve images similar to a composed query of a reference image and a description, without relying on training on triplet datasets. Existing methods for this task often rely on predefined, fixed retrieval processes that combine the image and the modified text through hand-crafted templates, which suffer from two main issues: non-adaptive retrieval queries and user-unfriendly retrieval processes. To address these limitations, we propose a novel framework - Automatic Multi-Agent Collaboration for Zero-Shot Composed Image Retrieval (AutoCIR). AutoCIR consists of three training-free agents - a planner, a retriever, and a corrector - that work together to iteratively identify and rectify mismatches. The planner guides the retriever by generating a customized target caption for the composed query and further refines this caption to resolve any semantic discrepancies based on feedback. The corrector, equipped with a chain-of-thought reasoning mechanism, conducts an in-depth evaluation of the retrieved results and generates appropriate self-correction actions. Extensive experiments on three benchmarks demonstrate that AutoCIR consistently outperforms previous competitive methods for ZS-CIR.

Traditional multimodal learning approaches often assume that all modalities are available during both the training and inference phases. However, this assumption is often impractical in real-world scenarios due to challenges such as sensor failures, data corruption, or privacy concerns. While recent efforts focus on enhancing the robustness of pre-trained Multimodal Transformers (MTs) under missing modality conditions, mainstream work in this field often overlook reconstructing the missing modalities and rely on static, sample-agnostic prompt-tuning techniques, undermining their efficacy in severe modality missing scenarios. To address these limitations, we propose REDEEM, a novel REtrieval-guiDEd conditional gEnerative fraMework that largely alleviates the modality missing problems on pre-trained MTs. REDEEM consists of a new adaptive retrieval mechanism to identify relevant instances for both modality-complete and -incomplete samples. It then conditions on the remaining modalities and utilizes the retrieved data as experts to effectively recover the missing ones in modality-incomplete instances through a within-modal reconstruction manner. Finally, REDEEM generates sample-aware inter-modal prompts from the retrieved instances to guide MTs in tackling severe modality missing challenges. Comprehensive experiments on three diverse multimodal classification benchmarks demonstrate that REDEEM significantly outperforms competitive baselines.

Micro-video popularity prediction (MVPP) is a crucial research topic with important implications for social media marketing and stakeholders. Current works in MVPP utilized the pre-trained vision-language models (PVLs) to model the multimodal features for prediction, failing to capture the evolving popularity trend in micro-videos and leading to suboptimal results. To tackle this limitation, we propose EvoPro, an Evolution-aware Prompt-augmented framework that enhances MVPP. First, inspired by the powerful multimodal understanding and text generation skills of Large Multimodal Models (LMMs), an LMM-driven generative retriever is proposed to create contextually rich retrieval queries and perform precise video-to-video retrieval, forming dynamic micro-video support sets that effectively reflect evolving patterns. Building upon this, a graph-based prompter generates evolutionary prompts by capturing the relational structures within the support set. These prompts, representing the latest trend dynamics, serve as few-shot examples to guide PVLs. By integrating evolutionary prompts, the PVLs are empowered to model the evolving popularity trends more accurately, yielding stronger and more predictive representations. Extensive experiments conducted on three benchmarks demonstrate that EvoPro significantly outperforms competitive baselines.

The rapid spread of rumor content on online micro-video platforms poses significant threats to public health and safety. However, existing Micro-Video Rumor Detection (MVRD) methods are generally black-box, which lacks transparency and makes it difficult to understand the reasoning behind classification decisions. In this work, we introduce ExMRD, a novel Explainable Micro-video Rumor Detection framework designed to generate detailed and coherent explanations for enhancing MVRD. Inspired by the powerful reasoning capacity of Chain-of-Thought (CoT), we introduce a novel inference mechanism called R3CoT-- consisting of Refining, Retrieving, and Reasoning on MVRD. This mechanism enables Multimodal Large Language Models (MLLMs) to reorganize the original video content, retrieve domain knowledge related to rumors, and generate explainable conclusions regarding whether the micro-video contains rumor information. Instead of directly fine-tuning MLLMs for MVRD, which is computationally expensive, we propose a Small Language Reviewer (SLReviewer), which distills the outputs of R3CoT guided MLLMs to ensure efficient and reliable predictions. Extensive experiments on three real-world benchmarks demonstrate that ExMRD significantly outperforms competitive baselines while providing high-quality rationales.

Short Video Hate Detection (SVHD) is increasingly vital as hateful content -such as racial and gender-based discriminationspreads rapidly across platforms like TikTok, YouTube Shorts, and Instagram Reels.Existing approaches face significant challenges: hate expressions continuously evolve, hateful signals are dispersed across multiple modalities (audio, text, and vision), and the contribution of each modality varies across different hate content.To address these issues, we introduce MoRE (Mixture of Retrievalaugmented multimodal Experts), a novel framework designed to enhance SVHD.MoRE employs specialized multimodal experts for each modality, leveraging their unique strengths to identify hateful content effectively.To ensure model's adaptability to rapidly evolving hate content, MoRE leverages contextual knowledge extracted from relevant instances retrieved by a powerful joint multimodal video retriever for each target short video.Moreover, a dynamic sample-sensitive integration network adaptively adjusts the importance of each modality on a per-sample basis, optimizing the detection process by prioritizing the most informative modalities for each instance.Our MoRE adopts an end-to-end training strategy that jointly optimizes both expert networks and the overall framework, resulting in nearly a twofold improvement in training efficiency, which in turn enhances its applicability to real-world scenarios.Extensive experiments on three benchmarks demonstrate that MoRE surpasses state-of-the-art baselines, achieving an average improvement of 6.91% in macro-F1 score across all datasets.

Accurately predicting the popularity of micro-videos is crucial for real-world applications such as recommender systems and identifying viral marketing opportunities. Existing methods often focus on limited cross-modal information within individual micro-videos, overlooking the potential advantages of exploiting vast repository of past videos. We present MMRA, a multi-modal retrieval-augmented popularity prediction model that enhances prediction accuracy using relevant retrieved information. MMRA first retrieves relevant instances from a multi-modal memory bank, aligning video and text through transformation mechanisms involving a vision model and a text-based retriever. Additionally, a multi-modal interaction network is carefully designed to jointly capture cross-modal correlations within the target video and extract informative knowledge through retrieved instances, ultimately enhancing the prediction. Extensive experiments conducted on the real-world micro-video dataset demonstrate the superiority of MMRA when compared to state-of-the-art models. The code and data are available at https://github.com/ICDM-UESTC/MMRA.