Partially Relevant Video Retrieval

Updated 16 October 2025

Partially Relevant Video Retrieval (PRVR) is a paradigm that retrieves untrimmed videos by focusing on segments that semantically match a query instead of the entire video.
It employs weak or multiple-instance learning with multi-scale aggregation and dynamic alignment to handle temporal sparsity and partial relevance in long videos.
PRVR is pivotal for applications in web-scale video libraries, surveillance, and broadcast archives, driving advances in efficient multimedia indexing.

Partially Relevant Video Retrieval (PRVR) denotes the retrieval paradigm where, given a textual or multimodal query, the retrieval system must identify untrimmed videos that contain only segment(s) relevant to the query. In contrast to conventional text-to-video retrieval (T2VR), which assumes that each video entirely matches the caption or query, PRVR relaxes this assumption and explicitly targets real-world scenarios where videos are long, untrimmed, and only a part—often not temporally localized a priori—is pertinent to the semantic intent of the query. This problem formulation closely matches practical search and multimedia indexing challenges, as found in domains such as web-scale video libraries, surveillance, and broadcast archives.

1. Motivation and Distinction from Traditional Paradigms

The foundation for PRVR lies in recognizing the limitations of instance-based retrieval (Wray et al., 2021) and short, pre-trimmed video datasets, which do not reflect the sparsity of actual content-to-query alignment observed in realistic applications (Dong et al., 2022). In such settings:

Videos are untrimmed, often minutes to hours long, and queries may map to brief events or actions embedded within.
A query may map to multiple partially- or semantically-relevant videos with variable content overlap, and many videos are irrelevant except for brief temporal segments.
Instance-level binary relevance is inadequate; continuous similarity gradations and ranking robustness with respect to partial matches are critical.

Traditional T2VR and Video Corpus Moment Retrieval (VCMR) focus on retrieving either holistic videos or precise temporal moments. PRVR specifically retrieves whole, untrimmed videos based on partial correspondence but is distinct from moment retrieval, which outputs time-localized intervals (Dong et al., 2022, Hou et al., 21 Feb 2024).

2. Methodological Foundations: Modeling and Training for Partial Relevance

PRVR methodology is rooted in two technical perspectives:

Weak- or Multiple-Instance Learning Formulations: Treating each video as a bag of temporal instances (frames and/or clips) (Dong et al., 2022, Wang et al., 2023), adopting coarse-to-fine similarity mechanisms, or event decomposition (Hou et al., 21 Feb 2024, Zhu et al., 1 Jun 2025).
Relaxed Notions of Relevance: Replacing strict pairwise supervision with similarity proxies estimated via semantic similarity, noun/verb class sharing, or learned soft relevance functions (Wray et al., 2021, Falcon et al., 2022).

Semantic Similarity Estimation:

PRVR leverages a continuous semantic similarity function $S_S(x_i, y_j) \in [0,1]$ , generalizing relevance from the binary instance-level metric $S_I$ :

$S_S(x_i, y_j) = 1$ if original pair,
$S_S(x_i, y_j) = S'(y_i, y_j),$ otherwise, with $S’$ derived from Bag-of-Words IoU, PoS matching, synset-aware overlap, or METEOR scores (Wray et al., 2021).

Data and Proxy Mining:

Pseudo-positive, ambiguous, and pseudo-negative samples are systematically constructed by mining inter-sample correlations (i.e., identifying unpaired, high-semantic-overlap video–query pairs) and intra-sample redundancy (i.e., recognizing redundant or background moments within a video which can serve as hard negatives) (Ren et al., 28 Apr 2025, Cho et al., 9 Jun 2025).

Loss Functions and Supervision:

Relevance-Weighted Losses: Triplet or contrastive losses parameterized by variable margin or soft labels reflecting semantic similarity (e.g., relevance-based margin $\Delta_{a,p,n} = 1 - R(a, n)$ (Falcon et al., 2022)).
Multi-Granularity and Dual-Branch Optimization: Separate loss branches for frame, event, and clip similarity (Hou et al., 21 Feb 2024, Wang et al., 2023), dual-branch student–teacher or exploration–inheritance paradigms distilled from large vision–LLMs (Dong et al., 14 Oct 2025).
Dynamic Soft Target Refinement: Label interpolation between hard and model-predicted targets for more robust supervision under partial relevance (Dong et al., 14 Oct 2025).
Robust Alignment and Uncertainty Modeling: Representations parameterized by multivariate Gaussians with stochastic sampling (“proxy-level matching”) accompany confidence-weighted set-to-set alignments (Zhang et al., 1 Sep 2025).

3. Video Representation and Temporal Aggregation in PRVR

Explicitly capturing partial relevance necessitates modeling at multiple temporal scales:

Multi-Scale Aggregation: Extraction of hierarchical features at both clip and frame granularity. For instance, MS-SL computes max similarity at both clip- and frame-levels, and then fuses them via a weighted sum: $S(v,q) = \alpha \cdot S_c(v,q) + (1-\alpha) \cdot S_f(v,q)$ (Dong et al., 2022).
Implicit Clip Modeling: GMMFormer models inter-frame dependencies via Gaussian kernels of variable scale in transformer self-attention, allowing frames to attend more to their temporal neighbors, thereby learning multi-scale clip representations without expensive sliding-window construction (Wang et al., 2023).
Learned Span Anchors and Moment Discovery: AMDNet introduces learnable temporal anchors (center, width per anchor) combined with differentiable masking (e.g., Gaussian windows) to “discover” salient moments aligned to the query and to suppress backgrounds (Song et al., 15 Apr 2025).
Event and Prototype Modeling: Uneven event modeling clusters consecutive frames by semantic similarity to form events with non-uniform lengths, refining their representations using context-aware attention with respect to the text (Zhu et al., 1 Jun 2025). Prototypical approaches encode diverse video contexts into a fixed set of prototypes aligned to text via reconstruction and orthogonalization objectives (Moon et al., 17 Apr 2025).
Hierarchical and Hyperbolic Strategies: EventFormer applies dual-level encoding (frame/event) with anchor-based multi-head self-attention to better model local–global semantics (Hou et al., 21 Feb 2024). HLFormer leverages both Lorentzian (hyperbolic) and Euclidean attention, dynamically fused, with hierarchy-preserving constraints via entailment cones (Li et al., 23 Jul 2025).

Robust video–text alignment under partial relevance requires:

Adaptive Margin and Relevance Weighting: Margin in contrastive losses reflects estimated semantic proximate or shared classes (nouns, verbs), supporting richer multi-positive supervision and reducing hyper-parameter sensitivity (Falcon et al., 2022).
Ambiguity-Aware Learning: Multi-positive contrastive learning and dual margin losses account for ambiguous or uncertain video–query pairs that may partially or indirectly match (“ambiguity-restrained learning” (Cho et al., 9 Jun 2025)).
Prompt and Attention Innovations: ProPy builds a hierarchical prompt pyramid, incorporating prompt-based event representations at multiple scales within a frozen CLIP-based ViT backbone; dynamic ancestor-descendant interaction enforces rich semantic flows at all levels (Pan et al., 26 Aug 2025). Learnable confidence gates (RAL) explicitly down-weight uninformative query words in aggregating similarity scores (Zhang et al., 1 Sep 2025).

Conventional instance-based metrics (Recall@K, mean rank) are inappropriate in PRVR, as they do not account for degrees of relevance or the possibility of multiple partially relevant results. Metrics such as nDCG (computed over continuous relevance scores) and SumR (aggregate of recall metrics across ranks) are now standard (Wray et al., 2021, Hou et al., 21 Feb 2024).

5. Benchmark Datasets and Comparison of Representative Methods

Large-scale datasets supporting PRVR evaluation include:

TVR: TV show retrieval with multiple descriptive sentences per long clip (Dong et al., 2022, Hou et al., 21 Feb 2024).
ActivityNet Captions: User-generated videos with diverse event coverage (Hou et al., 21 Feb 2024, Wang et al., 2023).
Charades-STA: Indoor activity videos annotated with text and moments (Hou et al., 21 Feb 2024, Wang et al., 2023).
Specialized Benchmarks: UCFCrime-AR, XDViolence-AR for anomaly retrieval (Wu et al., 2023).

Recent methods and results (as reported) include: | Method | Core Innovations | Representative Gains / Features | |--------------------|--------------------------------------|-----------------------------------| | MS-SL (Dong et al., 2022) | Multi-scale similarity, MIL, key-clip | Outperforms T2VR baselines on TVR | | GMMFormer (Wang et al., 2023) | Gaussian mixture attention, query diverse loss | 2.5x faster, 20x less storage than MS-SL | | GMMFormer v2 (Wang et al., 22 May 2024) | Uncertainty-aware, optimal matching, TC-GMMBlock | +6-7% SumR over GMMFormer | | AMDNet (Song et al., 15 Apr 2025) | Active moment discovery, span anchors | 15.5x smaller, +6.0 SumR over SOTA| | UEM (Zhu et al., 1 Jun 2025) | Uneven event segmentation, CAER | +19% SumR over GMMFormer-v2 on TVR| | MamFusion (Ying et al., 4 Jun 2025) | Multi-Mamba state-space, bidir fusion | SOTA performance, robust to redundancy | | ProPy (Pan et al., 26 Aug 2025) | CLIP prompt pyramid, interaction | +7.7% TVR, +7.2% ActivityNet over SOTA| | RAL (Zhang et al., 1 Sep 2025) | Probabilistic Gaussian modeling, proxy matching, learned confidence | +9.7% SumR improvement on GMMFormer-v2| | DL-DKD++ (Dong et al., 14 Oct 2025) | Dual student branching, dynamic distillation | Balanced gains across all partial-relevance levels|

6. Open Challenges, Applications, and Future Research Directions

PRVR remains an active area of research with the following open problems and frontiers:

Robustness to Sequence Length, Ambiguity, and High Background Ratios: Continued focus on explicit modeling of uncertainty, ambiguity, and diverse event structures remains a priority, with probabilistic, ambiguity-restrained, and hyperbolic models providing key insights (Zhang et al., 1 Sep 2025, Cho et al., 9 Jun 2025, Li et al., 23 Jul 2025).
Efficient Retrieval under Scale: Strategies combining dynamic event/moment segmentation, compact representation (super-images (Nishimura et al., 2023), prototypes (Moon et al., 17 Apr 2025)), and lightweight distillation architectures (Dong et al., 14 Oct 2025) address storage, compute, and responsiveness for web-scale deployments.
Integration of Pretrained Multimodal Models: CLIP and similar VLMs, when adapted with prompt pyramids and fine-tuning frameworks, are proving powerful for PRVR with further potential in knowledge distillation and hybrid retrieval (Pan et al., 26 Aug 2025, Dong et al., 14 Oct 2025).
Unified Metrics and Benchmarks: Metrics aligned to the semantic similarity paradigm (nDCG, SumR), combined with diverse, real-world benchmarks (with/without frame/moment annotations), are now essential for the field (Wray et al., 2021, Hou et al., 21 Feb 2024).
From Video Retrieval to Fine-grained Video Understanding: PRVR techniques—especially those developed for flexible multi-event and ambiguity-aware fusion—are expected to impact related tasks: temporal action localization, question answering, dense captioning, and interactive summarization.

7. Significance and Outlook

PRVR shifts the video retrieval problem to a more nuanced and realistic paradigm, where performance depends on fine-grained, context-aware semantic matching, robust temporal modeling, and sophisticated evaluation procedures that reflect degrees of partial correspondence. Advances in adaptive representation, hierarchical/uncertainty modeling, efficient feature aggregation, and alignment are rapidly pushing the research boundaries and enhancing the deployability of retrieval systems in practice. As large pre-trained vision–LLMs and dynamic, prompt-based architectures mature, PRVR is positioned to serve as both a proving ground and a foundation for a wide spectrum of robust, semantically-aware multimedia search and understanding technologies.