Video-Text Retrieval

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

6 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 59 papers with code (111 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
  3 Oct 2023 6 repositories listed Syntology ran 7 of 14 samples · 7 unverified
  We thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M.
• CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
  18 Apr 2021 5 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 3 pointer-only (licence)
  In this paper, we propose a CLIP4Clip model to transfer the knowledge of the CLIP model to video-language retrieval in an end-to-end manner.
• Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
  1 Apr 2021 5 repositories listed Syntology ran 3 of 11 samples · 8 unverified
  Our objective in this work is video-text retrieval - in particular a joint embedding that enables efficient text-to-video retrieval.
• Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
  5 Jun 2023 4 repositories listed Syntology ran 18 of 25 samples · 7 unverified · 9 pointer-only (licence)
  We present Video-LLaMA a multi-modal framework that empowers Large Language Models (LLMs) with the capability of understanding both visual and auditory content in the video.
• Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
  1 Mar 2020 4 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  To improve fine-grained video-text retrieval, we propose a Hierarchical Graph Reasoning (HGR) model, which decomposes video-text matching into global-to-local levels.
• X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
  15 Jul 2022 3 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 1 pointer-only (licence)
  However, cross-grained contrast, which is the contrast between coarse-grained representations and fine-grained representations, has rarely been explored in prior research.
• mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
  24 May 2022 3 repositories listed
  Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks.
• InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
  21 Dec 2023 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  However, the progress in vision and vision-language foundation models, which are also critical elements of multi-modal AGI, has not kept pace with LLMs.
• RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos
  11 Dec 2023 2 repositories listed
  Adapting existing short video (5-30 seconds) grounding methods to this problem yields poor performance.
• TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
  4 Dec 2023 2 repositories listed Syntology ran 7 of 11 samples · 4 unverified
  This work proposes TimeChat, a time-sensitive multimodal large language model specifically designed for long video understanding.
• UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
  13 Feb 2023 2 repositories listed Syntology ran 8 of 10 samples · 2 unverified · 10 pointer-only (licence)
  Particularly, on the MSRVTT retrieval task, UniAdapter achieves 49.
• Egocentric Video-Language Pretraining
  3 Jun 2022 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 2 pointer-only (licence)
  Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention.
• Bridging Video-text Retrieval with Multiple Choice Questions
  13 Jan 2022 2 repositories listed Syntology ran 13 of 24 samples · 11 unverified · 6 pointer-only (licence)
  As an additional benefit, our method achieves competitive results with much shorter pre-training videos on single-modality downstream tasks, e.
• Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
  9 Sep 2021 2 repositories listed
  In this paper, we propose a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (CAMoE) and a novel Dual Softmax Loss (DSL) to solve the two heterogeneity.
• DiscoVLA: Discrepancy Reduction in Vision, Language, and Alignment for Parameter-Efficient Video-Text Retrieval
  10 Jun 2025 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  The parameter-efficient adaptation of the image-text pretraining model CLIP for video-text retrieval is a prominent area of research.
• One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory
  29 May 2025 1 repository listed
  We introduce grounded video tokenization, a paradigm that organizes tokens based on panoptic sub-object trajectories rather than fixed patches.
• LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts
  20 May 2025 1 repository listed
  To address these limitations, we introduce LoVR, a benchmark specifically designed for long video-text retrieval.
• Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding
  9 Feb 2025 1 repository listed
  To overcome these challenges, we introduce a specialized cognitive module, temporal working memory (TWM), which aims to enhance the temporal modeling capabilities of MFMs.
• Expertized Caption Auto-Enhancement for Video-Text Retrieval
  5 Feb 2025 1 repository listed
  Video-text retrieval has been stuck in the information mismatch caused by personalized and inadequate textual descriptions of videos.
• Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrieval
  26 Dec 2024 1 repository listed
  We further enhance the use of harder-negatives in model training, and benchmark a variety of video-text models on RTime.
• CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives
  29 Nov 2024 1 repository listed
  Grounding the instruction in the environment is a key step in solving language-guided goal-reaching reinforcement learning problems.
• Text Proxy: Decomposing Retrieval from a 1-to-N Relationship into N 1-to-1 Relationships for Text-Video Retrieval
  9 Oct 2024 1 repository listed
  By replacing a single text query with a series of text proxies, TV-ProxyNet not only broadens the query scope but also achieves a more precise expansion.
• Learning Video Context as Interleaved Multimodal Sequences
  31 Jul 2024 1 repository listed Syntology ran 1 of 3 samples · 2 unverified · 3 pointer-only (licence)
  In this paper, we introduce MovieSeq, a multimodal language model developed to address the wide range of challenges in understanding video contexts.
• Video-Language Alignment via Spatio-Temporal Graph Transformer
  16 Jul 2024 1 repository listed
  In this paper, we propose a novel Spatio-Temporal Graph Transformer module to uniformly learn spatial and temporal contexts for video-language alignment pre-training (dubbed STGT).
• Diving Deep into the Motion Representation of Video-Text Models
  7 Jun 2024 1 repository listed
  This method proves to be effective on two action datasets for the motion description retrieval task.
• vid-TLDR: Training Free Token merging for Light-weight Video Transformer
  20 Mar 2024 1 repository listed Syntology ran 3 of 4 samples · 1 unverified
  To tackle these issues, we propose training free token merging for lightweight video Transformer (vid-TLDR) that aims to enhance the efficiency of video Transformers by merging the background tokens without additional…
• M2-RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards Effective and Efficient Zero-shot Video-text Retrieval
  31 Jan 2024 1 repository listed
  We then summarize this empirical study into the M2-RAAP recipe, where our technical contributions lie in 1) the data filtering and text re-writing pipeline resulting in 1M high-quality bilingual video-text pairs, 2) the…
• ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval
  19 Dec 2023 1 repository listed
  Then, in Context-aware Simulator Learning stage, we train a Content-aware Prompt Simulator under a simulated test scenarios to produce the corresponding CaDP.
• Harvest Video Foundation Models via Efficient Post-Pretraining
  30 Oct 2023 1 repository listed
  Building video-language foundation models is costly and difficult due to the redundant nature of video data and the lack of high-quality video-language datasets.
• TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding
  29 Oct 2023 1 repository listed Syntology ran 11 of 15 samples · 4 unverified
  TESTA can reduce the number of visual tokens by 75% and thus accelerate video encoding.

Syntology lines on 15 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.