Video customization has gained significant interest, yet achieving temporal and geometric consistency remains challenging. To address this, we propose a novel framework that leverages neural representations to model dynamic 3D scenes in videos, distilling their knowledge into rendering consistency to regularize diffusion models. Our method conceptualizes dynamic scenes as 4D volumes, employing grid-based dynamic geometry representations to enhance geometric consistency and cross-view appearance representations to improve visual coherence across frames. To seamlessly integrate these representations into diffusion models, we propose a latent neural rendering strategy that aligns geometry and appearance in a unified manner. Comprehensive evaluations across tasks such as local object editing and composite customization reveal consistent and significant performance gains, demonstrating the effectiveness and robustness of our proposed method.
The effective modeling and querying of the world’s ever-growing collection of multimedia is a significant challenge. Multimodal knowledge graphs offer a powerful solution by integrating heterogeneous data sources and uncovering complex relationships. We point to its benefits across a range of applications, including holistic memory retrieval, interactive video exploration, cross-modal fact-checking for journalism, and data fusion for clinical diagnosis. We also explore its use in enhancing educational tools, product feedback analysis in e-commerce, and semantic annotation of textual corpora. By showcasing how these diverse applications model multimedia at its core, we illustrate the potential of a multimodal knowledge graph store that can directly leverage and process the content of multimedia. However, a major limitation of existing approaches is that they treat multimedia documents as opaque entities, which severely limits their analytical potential. In this paper, we introduce MeGraS, our novel multimodal knowledge graph store that embodies a new paradigm: to provide direct access to multimedia document content, enabling deeper and more flexible analysis.
No-Reference Image Quality Assessment (NR-IQA) methods has made significant progress with the rapid development of deep learning techniques. However, most NR-IQA methods usually use a single attention mechanism for feature interaction, which limits the prediction performances of these NR-IQA methods in complex scenarios. To address this, we propose a Feature Enhancement and Feature Interaction Network (FEFI-Net) for NR-IQA. The key contributions are the multi-scale attention enhancement module and the multi-attention feature interaction module. The multi-scale attention enhancement module is designed to enhance the pre-trained features extracted by Swin Transformer, thereby obtaining more comprehensive feature representations. The multi-attention feature interaction module with three attentions is designed to establish more accurate feature interaction. Comparative experiments on five widely-used IQA datasets demonstrate that our FEFI-Net surpasses some state-of-the-art NR-IQA methods in prediction accuracy.
The rapid dissemination of multimodal content has intensified the spread of fabricated news, presenting a substantial threat to social integrity. A formidable challenge for current detection systems is identifying misinformation related to novel events in zero-shot scenarios. Prevailing zero-shot methods typically assess news items in isolation via semantic matching, a strategy that fails to recognize the recycled disinformation tactics from past campaigns and lacks the sophisticated reasoning needed to identify subtle, cross -modal discrepancies. To surmount these deficiencies, we introduce MRAFnd, a novel Multimodal Retrieval-Augmented Framework for Zero-Shot Fake News Detection. MRAFnd emulates a collaborative team of analysts to verify news veracity. The framework initiates with Multimodal Similarity-based News Retrieval to assemble a corpus of contextually analogous articles from an unlabeled reference database. Subsequently, during the Bifurcated Evidential Reasoning stage, agents perform a dual -directional analysis to extract critical patterns from the retrieved evidence. Finally, a Multi-Agent Collaborative Debate, involving Analyst and Arbiter agents, engages in a structured discourse to arrive at a definitive and robust conclusion. Comprehensive experiments on three benchmark datasets reveal that MRAFnd markedly surpasses state-of-the-art baselines, achieving an accuracy gain of up to 2.35% on the demanding Weibo-21 dataset.
Language-guided video object segmentation (LVOS) has achieved remarkable progress through advanced video segmentation models, but their performance heavily relies on large-scale fine-grained training data. The acquisition and fine-grained annotation of video datasets require exorbitant labeling costs and considerable time overhead. To address this challenge, we present DiffSynth-LVOS, a novel method that contains a diffusion-based object video generation model to create synthetic various training data according to textual object description. Our method first generates high-quality videos conditioned on masks from existing real datasets by introducing a full-fill mask constraint and video latent blending module, ensuring precise frame-mask alignment and temporal consistency for video segmentation training. Then the impact of synthetic videos through joint fine-tuning with real videos is investigated to increase the performance of LVOS. Experimental results demonstrate significant performance improvements when training on our expanded dataset (e.g., 𝒥&ℱ score improves from 52.39
