Top Video & Multimodal Retrieval Papers - December 2, 2025

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Stay up-to-date with the cutting-edge advancements in video and multimodal retrieval! This article summarizes the latest research papers published on December 2, 2025, focusing on innovative approaches and breakthroughs in these exciting fields. For an enhanced reading experience and access to even more papers, be sure to visit the Github page.

Video Retrieval: Pushing the Boundaries of Video Understanding

Video retrieval is a rapidly evolving field, driven by the increasing volume of video data and the demand for efficient ways to search, access, and utilize this information. These latest papers highlight the diverse approaches researchers are taking to tackle the challenges of video understanding and retrieval, from enhancing temporal-semantic robustness to leveraging large language models (LLMs) for traffic video analysis.

Adaptive Evidential Learning for Temporal-Semantic Robustness in Moment Retrieval

Moment retrieval, a crucial task in video understanding, aims to identify specific moments within a video that correspond to a given query. This paper introduces an innovative approach called Adaptive Evidential Learning, designed to enhance the temporal-semantic robustness of moment retrieval systems. This method likely focuses on improving the system's ability to handle variations in timing and semantic content, ensuring accurate retrieval even when queries are imprecise or the video content is complex. Accepted by AAAI 2026, this research promises significant advancements in the precision and reliability of moment retrieval, making it easier to pinpoint the exact moments users are looking for. The paper details a comprehensive study, spanning 10 pages with 9 figures and 5 tables, offering a deep dive into the methodology and results achieved through Adaptive Evidential Learning, demonstrating its potential impact on the future of video search and analysis.

CourseTimeQA: Lecture-Video Benchmark and Latency-Constrained Cross-Modal Fusion

The CourseTimeQA paper introduces a novel benchmark specifically designed for lecture videos, addressing the unique challenges of timestamped question answering in educational content. The paper also proposes a latency-constrained cross-modal fusion method, which optimizes the integration of visual and textual information while adhering to strict latency requirements. This is particularly relevant for real-time applications where immediate responses are crucial. The research contributes significantly to the field by providing a valuable resource for evaluating video question-answering systems and presenting a practical solution for efficient information retrieval from lecture videos. With 5 figures and 8 tables, the paper thoroughly illustrates the benchmark setup, the proposed method, and the experimental results, offering a detailed analysis of the approach and its effectiveness in the context of educational video content.

See, Rank, and Filter: Word-Aware Clip Filtering via Scene Understanding

See, Rank, and Filter presents a novel approach to moment retrieval and highlight detection by incorporating scene understanding and word-aware clip filtering. This method likely focuses on identifying the most relevant video clips based on both the visual content and the textual context, ensuring that important moments are captured and ranked effectively. The research emphasizes the importance of understanding the scene's context and the meaning of words within the video, leading to more accurate and comprehensive video analysis. This approach has the potential to significantly enhance the performance of video retrieval systems by providing a more nuanced understanding of video content and user queries.

Enhanced Partially Relevant Video Retrieval with Coherence Prediction

This paper focuses on partially relevant video retrieval, a scenario where retrieved videos may only partially match the user's query. The proposed method leverages inter- and intra-sample analysis along with coherence prediction to improve retrieval accuracy. This approach likely involves analyzing the relationships between different video segments and predicting the overall coherence of the video with respect to the query, enabling the system to identify videos that are relevant even if they don't perfectly match the search terms. This research contributes significantly to the field by addressing the challenges of retrieving videos with partial relevance, offering a sophisticated method for enhancing the accuracy and usefulness of video search results.

Other Notable Video Retrieval Papers

  • Captain Safari: A World Engine: Explores the development of a virtual world engine, potentially for simulation or gaming applications.
  • ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering: Focuses on enhancing visual question answering by incorporating reasoning and knowledge augmentation techniques.
  • Qwen3-VL Technical Report: Provides a detailed technical overview of the Qwen3-VL model, a large-scale vision-language model with 42 pages of documentation.
  • Watch and Learn: Learning to Use Computers from Online Videos: Investigates methods for teaching AI agents how to use computers by learning from online videos.
  • F.A.C.U.L.: Language-Based Interaction with AI Companions in Gaming: Explores language-based interaction with AI companions in gaming environments, with a 14-page report including 11 figures.
  • Video-R4: Reinforcing Text-Rich Video Reasoning with Visual Rumination: Aims to improve video reasoning by reinforcing text-rich content with visual rumination techniques.
  • TrafficLens: Multi-Camera Traffic Video Analysis Using LLMs: Leverages Large Language Models (LLMs) for analyzing traffic videos from multiple cameras, presented at the 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC).
  • Unsupervised Memorability Modeling from Tip-of-the-Tongue Retrieval Queries: Proposes an unsupervised method for modeling video memorability based on tip-of-the-tongue retrieval queries, accepted at WACV 2026.
  • LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling: Explores the use of native tool calling to encourage more thoughtful processing of long videos.
  • Adversarial Robustness for Unified Multi-Modal Encoders via Efficient Calibration: Focuses on improving the robustness of multi-modal encoders against adversarial attacks through efficient calibration techniques.
  • Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations: Investigates methods for explaining video classifiers using counterfactual explanations, providing insights into how the classifiers make decisions.

Multimodal Retrieval: Bridging the Gap Between Modalities

Multimodal retrieval, which involves searching and retrieving information across different modalities such as text, images, and audio, is becoming increasingly important in today's data-rich world. These papers showcase the latest advancements in multimodal understanding and retrieval, focusing on techniques that can effectively integrate and leverage information from multiple sources.

Augmenting Intra-Modal Understanding in MLLMs for Robust Multimodal Keyphrase Generation

This paper addresses the challenge of generating accurate and relevant keyphrases from multimodal data by augmenting intra-modal understanding in Multimodal Large Language Models (MLLMs). This approach likely focuses on improving the model's ability to understand the individual modalities (e.g., text and images) before integrating them, leading to more robust and coherent keyphrase generation. The research highlights the importance of in-depth analysis within each modality as a crucial step towards effective cross-modal understanding and generation. By enhancing the model's understanding of individual modalities, this method promises to significantly improve the quality and relevance of generated keyphrases, making it easier to summarize and understand multimodal content.

CART: Generative Cross-Modal Retrieval with Coarse-To-Fine Semantic Modeling

The CART framework introduces a generative approach to cross-modal retrieval, utilizing coarse-to-fine semantic modeling to bridge the gap between different modalities. This method likely involves first creating a broad, high-level understanding of the content and then progressively refining the understanding to capture finer details, ensuring accurate and context-aware retrieval. The paper's updated baseline metrics, based on original publications, further strengthen the framework's validation and demonstrate its effectiveness in cross-modal information retrieval. The generative nature of CART allows it to create new representations that effectively link different modalities, enhancing its ability to retrieve relevant information across diverse data types.

Multilingual Training-Free Remote Sensing Image Captioning

This research presents a novel approach to multilingual remote sensing image captioning that requires no specific training. This method likely leverages existing knowledge and models to generate captions in multiple languages without the need for language-specific training data, making it highly adaptable and cost-effective. The ability to automatically generate captions for remote sensing images in various languages opens up new possibilities for analyzing and utilizing this data, especially in contexts where multilingual communication is essential. This approach significantly reduces the barriers to accessing and understanding remote sensing imagery, fostering broader applications in environmental monitoring, disaster response, and urban planning.

Hybrid-DMKG: Hybrid Reasoning over Dynamic Multimodal Knowledge Graphs for Multimodal Multihop QA with Knowledge Editing

Hybrid-DMKG proposes a hybrid reasoning framework that operates on dynamic multimodal knowledge graphs (DMKGs) to address multimodal multihop question answering (QA) with knowledge editing. This complex approach likely combines different reasoning techniques to navigate the knowledge graph and answer questions that require multiple steps of inference. The dynamic nature of the knowledge graph allows for updates and edits, ensuring that the system can adapt to new information and changing contexts. Accepted by AAAI 2026, this research represents a significant advancement in the field of multimodal QA, offering a robust and adaptable method for extracting information from complex data sources. The framework's ability to handle multihop reasoning and knowledge editing makes it particularly valuable for applications that require deep understanding and continuous learning.

Other Notable Multimodal Retrieval Papers

  • MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding: Explores the use of generative Multimodal Large Language Models (MLLMs) for learning multimodal representations, enhancing product understanding in e-commerce, accepted by WSDM 2026.
  • Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward: Investigates the relationship between understanding and generation in unified multimodal models, providing insights into future research directions.
  • CACARA: Cross-Modal Alignment Leveraging a Text-Centric Approach for Cost-Effective Multimodal and Multilingual Learning: Presents a cost-effective method for multimodal and multilingual learning by leveraging a text-centric approach to cross-modal alignment.
  • Look as You Think: Unifying Reasoning and Visual Evidence Attribution for Verifiable Document RAG via Reinforcement Learning: Focuses on unifying reasoning and visual evidence attribution for verifiable document retrieval-augmented generation (RAG) using reinforcement learning, presented as a poster at AAAI'2026.
  • Words into World: A Task-Adaptive Agent for Language-Guided Spatial Retrieval in AR: Explores the development of a task-adaptive agent for language-guided spatial retrieval in augmented reality (AR).
  • RealAppliance: Let High-fidelity Appliance Assets Controllable and Workable as Aligned Real Manuals: Aims to make high-fidelity appliance assets controllable and workable in alignment with real-world manuals.
  • LFM2 Technical Report: Provides a technical overview of the LFM2 model, potentially a large foundation model for multimodal applications.
  • See, Rank, and Filter: Important Word-Aware Clip Filtering via Scene Understanding: (Also listed in Video Retrieval) Highlights the importance of scene understanding and word-aware clip filtering for moment retrieval and highlight detection.
  • Resolving Evidence Sparsity: Agentic Context Engineering for Long-Document Understanding: Addresses the challenge of evidence sparsity in long documents by using agentic context engineering techniques.
  • Breaking the Visual Shortcuts in Multimodal Knowledge-Based Visual Question Answering: Focuses on mitigating visual shortcuts in multimodal knowledge-based visual question answering systems.
  • ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering: (Also listed in Video Retrieval) Emphasizes the use of reasoning-augmented generation for visual question answering.

Conclusion

The research papers published on December 2, 2025, demonstrate the vibrant and rapidly evolving nature of video and multimodal retrieval. From innovative approaches to moment retrieval and highlight detection to the development of robust multimodal question-answering systems, these papers offer valuable insights into the future of information access and understanding. As the volume of video and multimodal data continues to grow, these advancements will play a crucial role in enabling us to effectively search, utilize, and interact with this rich source of information.

To delve deeper into the world of video and multimodal retrieval, explore resources like the Association for Computational Linguistics (ACL), a leading organization for natural language processing research.