PHA-Net: Prototype-based hierarchical alignment network for text-video retrieval

Strengthen-then-retrieve: semantic enrichment and reconstruction feedback for text-video retrieval

Probabilistic Embeddings With Evidence Learning and Refinement for Text–Video Retrieval

This paper studies the problem of text-video retrieval, where the goal is to learn accurate cross-modal alignment between videos and text. This problem is challenging because of the matching ambiguity caused by the inherent gap between the heterogeneous video and text modalities. In particular, the differences in the information granularity and abstraction levels between the two modalities hinder a reliable sample-level alignment. Moreover, redundant visual content, sparse textual descriptions, and temporal variability in videos introduce additional uncertainty, resulting in ambiguous matching and suboptimal performance. In this paper, we propose a novel method named Probabilistic Embeddings with Evidence Learning and Refinement (PE2LR), which models video-text pairs as probability distributions and captures uncertainty through the evidence theory. Specifically, we perform distribution-level representation learning to resolve the semantic ambiguity of video-text pairs. To improve the alignment further, we introduce a distribution-based embedding refinement module to ameliorate the semantic consistency across modalities. The proposed PE2LR is able to pull positive sample pairs closer in the embedding space, while pushing the negative pairs apart. Comprehensive experiments on several benchmark datasets (including MSRVTT, DiDeMo, and ActivityNet Captions) demonstrate that our PE2LR achieves state-of-the-art search performance.

Large model-assisted video summarization via global entity unification and robust importance scoring

MultiModal Code-Switching: Interleaving Visual Objects into Language for Explicit Object-Level Alignment

Inspired by the linguistic phenomenon of code-switching, MMCS interleaves vision and language by replacing textual entities with their corresponding visual objects, enforcing local vision-language grounding.

Mitigating Modality and Language-Style Gaps for Zero-Shot Video Moment Retrieval

This work proposes Self-SiMS, a self-similarity-based Moment Proposal and Scoring that exploits intrinsic relationships within videos, enabling robust span generation and scoring and introduces a query-aware MLLM-based reasoning stage to further sharpen alignment between text and video.

Multimodal Video Understanding: A Capability-Based Survey of Alignment, Expression, and Reasoning

Multimodal video understanding (MVU) has emerged as a fast-growing research frontier, driven by major advances in video-language pre-training and large multimodal models over the past decade. MVU aims to synergistically integrate visual, audio and textual modalities to interpret complex video semantics, supporting widespread downstream tasks including cross-modal retrieval, dense captioning, video question answering, event analysis and intelligent assistance. Despite the rapid proliferation of specialized MVU models, the community still lacks a unified capability-centric framework to systematically clarify the hierarchical competency architecture and evolutionary trajectory of state-of-the-art approaches. To address this issue, this paper presents a structured, comprehensive survey of the latest MVU progress, establishing a novel three-tier taxonomy that categorizes existing studies into cross-modal alignment, multi-granularity semantic expression and multimodal reasoning. Along this pipeline, we further systematically synthesize core modality fusion strategies, mainstream benchmark datasets and standardized evaluation protocols. Through a fine-grained analysis of representative published results, we highlight the critical impact of inconsistent evaluation settings, cross-experiment comparability bottlenecks and inherent methodological trade-offs between performance and efficiency. Finally, we identify and dissect three key open challenges: ultra-long video scalability, performance degradation from modality noise and missing data, and factual reliability risks in generative MVU systems. This capability-oriented systematic reference clarifies the methodological evolution logic of MVU, and provides actionable guidance for developing next-generation robust, high-performance multimodal video understanding systems.