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arxiv: 2604.12148 · v1 · submitted 2026-04-13 · 💻 cs.CV

Recognition: unknown

ViLL-E: Video LLM Embeddings for Retrieval

Fan Fei, Jayakrishnan Unnikrishnan, Mubarak Shah, Rohit Gupta, Sheng Liu, Son Tran

Authors on Pith no claims yet

Pith reviewed 2026-05-10 14:55 UTC · model grok-4.3

classification 💻 cs.CV
keywords video retrievalvideo LLMembeddingstemporal localizationzero-shot retrievalcontrastive learningcomposed retrieval
0
0 comments X

The pith

ViLL-E adds a flexible embedding mechanism to VideoLLMs so they match specialized retrieval models and gain zero-shot search skills.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

VideoLLMs handle question-answering about videos well but fall short on retrieval tasks like finding videos from text descriptions. The paper introduces ViLL-E, which equips the model with an embedding generation process that lets it process complex videos longer before producing an output vector. Training happens in three stages that mix large-scale caption learning, detailed caption refinement, and multi-task fine-tuning on QA, localization, retrieval, and matching. If the approach works, a single model can handle both understanding and search jobs while also supporting new tasks such as searching videos with combined descriptions. This would reduce the need for separate specialized systems for different video applications.

Core claim

ViLL-E is a unified VideoLLM architecture with a novel embedding generation mechanism that allows the model to think longer for complex videos and stop early for easy ones. Trained via three-stage joint contrastive-generative learning on video-caption pairs, detailed captions, and a multi-task dataset, the model improves temporal localization by an average of 7 percent over other VideoLLMs, video retrieval by up to 4 percent over dual-encoder models, and reaches performance comparable to state-of-the-art specialized embedding models while staying competitive on VideoQA. The same training unlocks zero-shot composed video retrieval that beats prior methods by 5 percent and long-text retrieval

What carries the argument

The novel embedding generation mechanism that lets the model continue processing a video until it is ready to output an embedding vector.

If this is right

  • Temporal localization accuracy rises by about 7 percent on average compared with other VideoLLMs.
  • Video retrieval scores improve by as much as 4 percent over dual-encoder baselines and reach levels close to specialized embedding models.
  • Zero-shot composed video retrieval exceeds prior state-of-the-art by 5 percent.
  • Zero-shot retrieval from long text descriptions exceeds prior state-of-the-art by 2 percent.
  • Video question-answering performance stays competitive with dedicated models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • One model could replace separate pipelines for video search and video understanding in practical systems.
  • The same flexible processing idea might extend to audio or multimodal retrieval if the early-stopping logic generalizes.
  • The three-stage schedule offers a reusable pattern for turning other large language models into strong embedding generators.
  • If the mechanism scales, longer untrimmed videos could be handled without linear growth in compute per video.

Load-bearing premise

The three-stage training schedule together with the flexible embedding mechanism will produce the reported gains on new videos and tasks without overfitting to the chosen training and test sets.

What would settle it

Running the model on a fresh video retrieval benchmark drawn from sources never seen during any training stage and finding no gain over standard VideoLLMs or dual encoders would show the improvements do not generalize.

Figures

Figures reproduced from arXiv: 2604.12148 by Fan Fei, Jayakrishnan Unnikrishnan, Mubarak Shah, Rohit Gupta, Sheng Liu, Son Tran.

Figure 1
Figure 1. Figure 1: Left & Bottom Right: VideoLLMs lag expert models on some retrieval-based tasks, e.g. Temporal Localization (in green [17, 10, 15]), and are incapable of others, e.g. Text-to-Video Retrieval (in red [49, 1, 44]). A key difference between existing VideoLLMs and state of the art expert models in these tasks is the use of embeddings( Top Right). Our approach, ViLL-E (VideoLLM-Embed, pronounced willy) equips Vi… view at source ↗
Figure 2
Figure 2. Figure 2: Our model ViLL-E is a multi-modal LLM which has been equipped with additional embedding generation [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Three stages of training. Stage 1 (§ 3.4), the large scale joint contrastive and generative pre-training, which utilizes (a) Video-Text retrieval task and (b) Video captioning on the large scale Shutterstock video dataset. Stage 2 (§ 3.5), continues a similar joint pre-training approach (retrieval + captioning tasks as shown in (a) and (b)) on a smaller high quality dataset created using Claude-3-Sonnet. S… view at source ↗
Figure 4
Figure 4. Figure 4: Comparing original caption against Claude-3-Sonnet generated high-quality caption. 4.2 Stage 2: High Quality Intermediate Data We use a re-captioned subset of the Shutterstock dataset, whose extensively labeled keywords we utilize for the balancing operation. We exclude keywords with < 30 occurrences. Starting with the most frequent remaining keywords, we add a maximum of 500 videos per keyword to the cand… view at source ↗
Figure 5
Figure 5. Figure 5: Two Step Retrieval Inference. of videos is fed through a frozen spatio-temporal vision encoder; its output tokens are linearly pro￾jected into the hidden dimension of a large lan￾guage model (LLM) that has been lightweight-fine￾tuned via LoRA adapters (modules marked with fire symbol). These visual tokens are prepended to the textual prompt “<image> . . . <image> De￾scribe this video.” and processed by the… view at source ↗
Figure 6
Figure 6. Figure 6: Composed Video Retrieval Inference. L Intermediate Pre-training Data Balancing The Shutterstock dataset is extensively labelled with keywords, which we use to select a balanced set of videos for our intermediate pre-training dataset. Keywords with fewer than 30 occurrences are excluded, and up to 500 videos per remaining keyword are added to the candidate pool, starting with the most frequent keywords, to … view at source ↗
Figure 8
Figure 8. Figure 8: Our captions significantly increases the level [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: We ensure balance of concepts in our dataset [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Stage 1 Training. Joint Generative and Contrastive Pre-Training. This stage requires 2 forward passes of the model per step. In the first step, the videos along with prompt are passed to the model and the video caption and video embedding are generated simultaneously. In the second forward pass, only the captions are passed to generate caption embeddings. The ground-truth caption for the video serves as th… view at source ↗
Figure 10
Figure 10. Figure 10: Stage 3: Temporal Localization Task Training. Contextualized Text Embeddings (Left Half): Text em￾bedding are generated based on text input such as “Playing with glow slime in the dark.” A selection of sparse frames from the long video is sampled and added to the prompt to provide context. Sliding Window Video Clip Embeddings (Right Half): The long video is divided into clips via a sliding window mechanis… view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative Video Captioning results on MSR-VTT [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Illustrating our model’s retrieval embeddings and their alignment in various sub-domains of videos 20 [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
read the original abstract

Video Large Language Models (VideoLLMs) excel at video understanding tasks where outputs are textual, such as Video Question Answering and Video Captioning. However, they underperform specialized embedding-based models in Retrieval tasks, such as Text-toVideo Retrieval and Moment Retrieval. We introduce ViLL-E (Video-LLM-Embed), a unified VideoLLM architecture endowed with a novel embedding generation mechanism that allows the model to "think longer" for complex videos and stop early for easy ones. We train this model with a three-stage training methodology combining generative and contrastive learning: initial large-scale pre-training with video-caption pairs; followed by continual training on a smaller, detailed-caption dataset; and concluding with task-specific fine-tuning on a novel multi-task dataset covering Video QA, Temporal Localization, Video Retrieval, and Video-Text Matching. Our model significantly improves temporal localization (on avg. 7% over other VideoLLMs) and video retrieval (up to 4% over dual encoder models), achieving performance comparable to state-of-the-art specialized embedding models while remaining competitive on VideoQA tasks. Furthermore, our joint contrastive-generative training unlocks new zero-shot capabilities, significantly outperforming state-of-the-art methods in composed video retrieval (+5% over SotA) and retrieval from long text (+2% over SotA).

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces ViLL-E, a unified VideoLLM architecture with a novel adaptive embedding generation mechanism that permits the model to 'think longer' on complex videos and stop early on simple ones. It is trained via a three-stage pipeline (large-scale video-caption pre-training, detailed-caption continual training, and multi-task fine-tuning on VideoQA, temporal localization, retrieval, and matching) that combines generative and contrastive objectives. The authors report average 7% gains in temporal localization over other VideoLLMs, up to 4% gains in video retrieval over dual-encoder baselines, performance comparable to specialized embedding models, competitive VideoQA results, and new zero-shot capabilities in composed video retrieval (+5% over SotA) and long-text retrieval (+2% over SotA).

Significance. If the gains are shown to be robust and specifically attributable to the adaptive embedding mechanism rather than training volume alone, the work would be significant for unifying generative and retrieval capabilities in a single VideoLLM. The joint contrastive-generative training and variable-length thinking approach could reduce reliance on separate specialized models while enabling new zero-shot behaviors.

major comments (2)
  1. [Experiments section (Tables reporting main results and any ablation tables)] The central claims attribute the 7% temporal localization and 4% retrieval improvements (plus new zero-shot capabilities) to the combination of the adaptive 'think longer' embedding mechanism and the three-stage training. However, no ablation is presented that holds the training data, stages, and objectives fixed while removing or replacing the variable-length embedding mechanism with a standard fixed-length VideoLLM baseline. This control is load-bearing for the novelty and attribution claims in the abstract and experiments.
  2. [Abstract] The abstract states 'on avg. 7%' improvement and 'up to 4%' without specifying the exact metrics (e.g., R@1, mAP), number of datasets averaged, number of runs, or statistical significance. These details are required to assess whether the reported margins are reliable and generalizable.
minor comments (2)
  1. [Method section] Clarify the precise stopping criterion and implementation details of the adaptive embedding length (e.g., any learned threshold or entropy-based rule) so that the mechanism can be reproduced.
  2. [Experiments section] Add error bars or standard deviations to all quantitative tables and figures to support the percentage improvements.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point-by-point below, indicating where revisions will be made to strengthen the paper's clarity and attribution of results.

read point-by-point responses

  1. Referee: [Experiments section (Tables reporting main results and any ablation tables)] The central claims attribute the 7% temporal localization and 4% retrieval improvements (plus new zero-shot capabilities) to the combination of the adaptive 'think longer' embedding mechanism and the three-stage training. However, no ablation is presented that holds the training data, stages, and objectives fixed while removing or replacing the variable-length embedding mechanism with a standard fixed-length VideoLLM baseline. This control is load-bearing for the novelty and attribution claims in the abstract and experiments.

    Authors: We agree that an ablation isolating the adaptive embedding mechanism while holding the training data, stages, and objectives fixed is necessary to robustly attribute the gains. In the revised manuscript, we will add this controlled comparison by training and evaluating a fixed-length VideoLLM baseline under the identical three-stage pipeline and report the results alongside the main tables in the Experiments section. This will directly address the load-bearing nature of the claim. revision: yes

  2. Referee: [Abstract] The abstract states 'on avg. 7%' improvement and 'up to 4%' without specifying the exact metrics (e.g., R@1, mAP), number of datasets averaged, number of runs, or statistical significance. These details are required to assess whether the reported margins are reliable and generalizable.

    Authors: We acknowledge the need for greater precision in the abstract. We will revise it to explicitly state the metrics (e.g., average mAP for temporal localization across the evaluated datasets and R@1 for retrieval), the number of datasets over which the averages are computed, and note that results are from single runs given the computational demands of VideoLLM training. We will also clarify the absence of statistical significance testing. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical architecture and training claims with no derivation chain

full rationale

The paper introduces an architecture (adaptive embedding generation allowing variable 'thinking' time) and a three-stage training pipeline (pre-training, continual training, multi-task fine-tuning), then reports empirical gains on retrieval, localization, and zero-shot tasks. No equations, first-principles derivations, or predictions are described that could reduce to inputs by construction. Performance numbers are presented as experimental outcomes rather than derived quantities. Any self-citations (if present in full text) are not invoked to justify uniqueness theorems or ansatzes that would create load-bearing circularity. The central claims rest on comparative benchmarks, not on self-referential definitions or fitted parameters renamed as predictions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the model is described at a high level without equations or implementation specifics.

pith-pipeline@v0.9.0 · 5544 in / 1181 out tokens · 37986 ms · 2026-05-10T14:55:18.719523+00:00 · methodology

discussion (0)

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