Recent advances in long video understanding typically mitigate visual redundancy through visual token pruning based on attention distribution. However, while existing methods employ post-hoc low-response token pruning in decoder layers, they overlook the input-level semantic correlation between visual tokens and instructions (query). In this paper, we propose QuoTA, an ante-hoc training-free modular that extends existing large video-language models (LVLMs) for visual token assignment based on query-oriented frame-level importance assessment. The query-oriented token selection is crucial as it aligns visual processing with task-specific requirements, optimizing token budget utilization while preserving semantically relevant content. Specifically, (i) QuoTA strategically allocates frame-level importance scores based on query relevance, enabling one-time visual token assignment before cross-modal interactions in decoder layers, (ii) we decouple the query through Chain-of-Thoughts reasoning to facilitate more precise LVLM-based frame importance scoring, and (iii) QuoTA offers a plug-and-play functionality that extends to existing LVLMs. Extensive experimental results demonstrate that implementing QuoTA with LLaVA-Video-7B yields an average performance improvement of 3.2% across six benchmarks (including Video-MME and MLVU) while operating within an identical visual token budget as the baseline.

Existing large video-language models (LVLMs) struggle to comprehend long videos correctly due to limited context. To address this problem, fine-tuning long-context LVLMs and employing GPT-based agents have emerged as promising solutions. However, fine-tuning LVLMs would require extensive high-quality data and substantial GPU resources, while GPT-based agents would rely on proprietary models (e.g., GPT-4o). In this paper, we propose Video Retrieval-Augmented Generation (Video-RAG), a training-free and cost-effective pipeline that employs visually-aligned auxiliary texts to help facilitate cross-modality alignment while providing additional information beyond the visual content. Specifically, we leverage open-source external tools to extract visually-aligned information from pure video data (e.g., audio, optical character, and object detection), and incorporate the extracted information into an existing LVLM as auxiliary texts, alongside video frames and queries, in a plug-and-play manner. Our Video-RAG offers several key advantages: (i) lightweight with low computing overhead due to single-turn retrieval; (ii) easy implementation and compatibility with any LVLM; and (iii) significant, consistent performance gains across long video understanding benchmarks, including Video-MME, MLVU, and LongVideoBench. Notably, our model demonstrates superior performance over proprietary models like Gemini-1.5-Pro and GPT-4o when utilized with a 72B model.

With the rapid progress of large language models (LLMs) and diffusion models, there has been growing interest in personalized content generation. However, current conversational systems often present the same recommended content to all users, falling into the dilemma of "one-size-fits-all." To break this limitation and boost user engagement, in this paper, we introduce PCG (**P**ersonalized Visual **C**ontent **G**eneration), a unified framework for personalizing item images within conversational systems. We tackle two key bottlenecks: the depth of personalization and the fidelity of generated images. Specifically, an LLM-powered Inclinations Analyzer is adopted to capture user likes and dislikes from context to construct personalized prompts. Moreover, we design a dual-stage LoRA mechanism—Global LoRA for understanding task-specific visual style, and Local LoRA for capturing preferred visual elements from conversation history. During training, we introduce the visual content condition method to ensure LoRA learns both historical visual context and maintains fidelity to the original item images. Extensive experiments on benchmark conversational datasets—including objective metrics and GPT-based evaluations—demonstrate that our framework outperforms strong baselines, which highlight its potential to redefine personalization in visual content generation for conversational scenarios like e-commerce and real-world recommendation.

The emergence of personalized generation has made it possible to create texts or images that meet the unique needs of users.Recent advances mainly focus on style or scene transfer based on given keywords.However, in e-commerce and recommender systems, it is almost an untouched area to explore user historical interactions, automatically mine user interests with semantic associations, and create item representations that closely align with user individual interests.In this paper, we propose a brand new framework called Interest Augmented Multimodal Generator (I-AM-G).The framework first extracts tags from the multimodal information of items that the user has interacted with, and the most frequently occurred ones are extracted to rewrite the text description of the item.Then, the framework uses a decoupled text-to-text and image-to-image retriever to search for the top-K similar item text and image embeddings from the item pool.Finally, the Attention module for user interests fuses the retrieved information in a cross-modal manner and further guides the personalized generation process collaborating with the rewritten text.We conducted extensive and comprehensive experiments to demonstrate that our framework can effectively generate results aligned with user preferences, which potentially provides a new paradigm of Rewrite and Retrieve for personalized generation.

Text-to-Video Retrieval is a typical cross-modal retrieval task that has been studied extensively under a conventional supervised setting. Recently, some works have sought to extend the problem to a weakly supervised formulation, which can be more consistent with real-life scenarios and more efficient in annotation cost. In this context, a new task called Partially Relevant Video Retrieval (PRVR) is proposed, which aims to retrieve videos that are partially relevant to a given textual query, i.e., the videos containing at least one semantically relevant moment. Formulating the task as a Multiple Instance Learning (MIL) ranking problem, prior arts rely on heuristics algorithms such as a simple greedy search strategy and deal with each query independently. Although these early explorations have achieved decent performance, they may not fully utilize the bag-level label and only consider the local optimum, which could result in suboptimal solutions and inferior final retrieval performance. To address this problem, in this paper, we propose to exploit the relationships between instances to boost retrieval performance. Based on this idea, we creatively put forward: (1) a new matching scheme for pairing queries and their related moments in the video; and (2) a new loss function to facilitate cross-modal alignment between two views of an instance. Extensive validations on three publicly available datasets have demonstrated the effectiveness of our solution and verified our hypothesis that modeling instance-level relationships is beneficial in the MIL ranking setting. Our code will be publicly available at https://github.com/xjtupanda/BGM-Net .

Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs. When solving this problem, previous works generally lack in considering the structures of human faces and the correspondence between expressions and relevant facial muscles. To address this issue for better performance of ME spotting, this paper seeks to extract finer spatial features by modeling the relationships between facial Regions of Interest (ROIs). Specifically, we propose a graph convolutional-based network, called Action-Unit-aWare Graph Convolutional Network (AUW-GCN). In addition, to incorporate prior knowledge and address the issue of small datasets, AU-related statistics are encoded into the network. Comprehensive experiments show that our results outperform baseline methods consistently and achieve new SOTA performance in two benchmark datasets, CAS(ME)2and SAMM-LV. Our code is available at https://github.com/xjtupanda/AUW-GCN.

As a typical psychological stress reaction, micro-expression (ME) is usually quickly leaked on a human face and can reveal the true feeling and emotional cognition. Therefore,automatic ME analysis (MEA) has essential applications in safety, clinical and other fields. However, the lack of adequate ME data has severely hindered MEA research. To overcome this dilemma and encouraged by current image generation techniques, this paper proposes a fine-grained ME generation method to enhance ME data in terms of data volume and diversity. Specifically, we first estimate non-linear ME motion using thin-plate spline transformation with a dense motion network. Then, the estimated ME motion transformations, including optical flow and occlusion masks, are sent to the generation network to synthesize the target facial micro-expression. In particular, we obtain the relative action units (AUs) of the source ME to the target face as a constraint to encourage the network to ignore expression-irrelevant movements, thereby generating fine-grained MEs. Through comparative experiments on CASME II, SMIC and SAMM datasets, we demonstrate the effectiveness and superiority of our method. Source code is provided in https://github.com/MEA-LAB-421/MEGC2022-Generation.