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Video Captioning in Low-Light Conditions through Efficient Uncertainty-Aware Caption Correction

Arefeh Rezaei

arXiv:2609.31697v1cs.CV

TL;DR

Low-light conditions can make VLM-generated action descriptions incomplete or inaccurate. This work corrects the intermediate representation with a sparse Gaussian-process module that scales residual corrections using predictive uncertainty. Experiments on ARID report small residual prediction errors and qualitative improvements in incorrect or missing action descriptions.

  • Problem

    Low-light visual degradation can lead VLMs to produce incomplete, inaccurate, or semantically incorrect descriptions of observed actions.

    Source: §1 · p. 1

  • Method

    A sparse Gaussian-process module estimates the residual to a verified target representation and adds an uncertainty-scaled correction before language generation, without fine-tuning VideoChat2.

    Source: §1 · p. 2

  • Results

    Experiments on ARID report small residual prediction errors, consistent statistical characteristics between predicted and target residuals, and qualitative improvements in incorrect or missing action descriptions.

    Source: §6 · p. 13

  • Takeaways & Limitations

    The framework improves action-related caption content while preserving the underlying VLM, and is described as applicable to compatible architectures beyond VideoChat2.

    Source: §6 · p. 13

    Inaccurate residual predictions may affect the final corrected representation, although the correction magnitude adapts to predictive behavior without ground-truth representations at inference.

    Source: §4.5 · p. 9

Abstract

from arXiv · show

Low-light conditions can significantly degrade the ability of vision-language models (VLMs) to accurately describe human actions in videos. In this work, I propose an efficient uncertainty-aware representation correction framework for improving captions generated by VideoChat2 under real-world low-light conditions. Instead of fine-tuning the underlying VLM, the proposed framework introduces a lightweight sparse Gaussian process-based error estimation module between the projection layer and the language model to correct the intermediate representation. The correction module learns to estimate the residual between the original projected representation and a verified target representation, which is then adaptively scaled using a newly formulated uncertainty-aware coefficient and added to the original representation. To further improve residual estimation, I introduce a partitioned combined-kernel design. The correction model is trained separately using only 44 samples from the ARID dataset and requires only a small additional computational overhead during inference. The effectiveness of the proposed correction is evaluated through quantitative residual prediction and qualitative analysis of the generated captions. Although VideoChat2 is used in my experiments, the proposed framework is designed to be applicable to other compatible VLM architectures. \textbf{Code Availability: The implementation accompanying this work is publicly available at}:\href{https://github.com/areferezaee/Low-rank-SVGP-NP-update}{https://github.com/areferezaee/Low-rank-SVGP-NP-update}

1 Introduction

Low illumination can impair action descriptions from vision-language models, motivating correction of their intermediate representations rather than fine-tuning the full model. The proposed framework estimates residuals with a sparse Gaussian process and uses predictive uncertainty to adjust corrections before language generation.

1 Introduction

  • Low illumination can degrade action understanding and lead VLMs to generate incomplete, inaccurate, or semantically incorrect action descriptions in real-world videos.ARID was introduced to study human-action understanding under real-world low-light conditions.Source: §1 · p. 1
  • The framework inserts a residual-estimation module between VideoChat2’s projection layer and language model, correcting projected representations without changing the underlying VLM.It estimates the residual to a verified target representation, scales that estimate with a sample-dependent uncertainty-aware coefficient, and adds it to the original representation.Source: §1 · p. 2
  • The residual estimator uses sparse Gaussian-process regression with a partitioned combined-kernel design and sample-dependent uncertainty-aware correction strength.The projected feature representation is divided into segments processed through multiple kernel components.Source: §1 · p. 2
  • The correction model is trained separately on a small number of ARID samples, avoiding modification of VideoChat2 and allowing potential use with other compatible VLM architectures.VideoChat2 is the primary model in this study; broader applicability is stated for architectures with compatible intermediate representations.Source: §1 · p. 2
  • Evaluation on real-world low-light ARID videos covers representation-level residual prediction and resulting changes in action-related caption content.The introduction describes the training set as small but does not provide a sample count in these passages.Source: §1 · p. 2

2 Background and Preliminaries

The background describes ARID’s low-light action-recognition setting and VideoChat2’s intermediate representation, then motivates Gaussian-process prediction and uncertainty as components of representation correction.

2.1 ARID

  • ARID v1.5 supplies real-world low-light videos for caption experiments, although the dataset was designed for action recognition and has no caption annotations.Verified caption targets are constructed for the experiments; the VideoChat2 setup uses Phi-3 Mini as its downstream language model.Source: §2.1 · p. 2, §2.2 · p. 2
  • The correction module is inserted between VideoChat2’s projection stage and Phi-3 Mini, operating on the projected representation without modifying the visual encoder or fine-tuning the language model.The corrected representation is supplied to Phi-3 Mini for caption generation.Source: §2.2 · pp. 2–3

2.3 Gaussian Process Regression

  • Gaussian-process regression models predictive distributions, providing both a prediction and associated uncertainty rather than only a point estimate.The section defines the GP through a mean function and covariance kernel, with kernel values forming the covariance matrix.Source: §2.3 · p. 3
  • Sparse Gaussian processes use inducing points to reduce inference burden, supporting their use with the relatively high-dimensional representations considered here.Predictive uncertainty is used to adapt the magnitude of representation correction.Source: §2.3 · p. 3, §2.4 · p. 3

2.4 Gaussian-Process-Based Prediction and Uncertainty Modeling

  • The proposed estimator predicts the residual between the projected representation and its verified target, using predictive uncertainty to set correction magnitude for each sample.The correction model operates independently of the VLM and is designed for residual estimation with a small number of training samples.Source: §2.4 · p. 3

3 Related Work

Prior low-light video work focuses on action-category recognition, while this paper addresses language-based action descriptions through representation correction. It positions the approach as a fixed-VLM alternative to input enhancement or model retraining.

3.1 Low-Light Video Understanding

  • Earlier low-light approaches target predefined action recognition, including attention-based recognition and joint dark-video enhancement with recognition.These approaches address recognition from degraded visual information, whereas the present work targets language-based action descriptions.Source: §3.1 · p. 4
  • The proposed approach corrects the intermediate representation immediately before language generation, rather than enhancing the input video or retraining the underlying recognition model.This places the intervention at the representation-to-language interface.Source: §3.1 · p. 4

3.2 Video-Language Models and Video Captioning

  • Video-language models can generate natural-language video descriptions, but their descriptions may still reflect errors in underlying visual representations under challenging visual conditions.The paper uses VideoChat2, which connects video representations to a large language model through an intermediate multimodal interface.Source: §3.2 · p. 4
  • The approach keeps the underlying VLM fixed and refines its projected representation before the language model receives it.This differs from conventional model adaptation strategies described in the passage.Source: §3.2 · p. 4

4 Proposed Method

The framework inserts a sparse Gaussian-process error estimator between VideoChat2’s projection stage and language model, using predictive uncertainty to scale residual corrections. It constructs targets from verified captions, uses a low-rank sparse-GP formulation and partitioned kernels, and integrates the corrected representation without modifying VideoChat2’s parameters.

4.1 Overview of the Proposed Framework

  • A sparse-GP module estimates residuals from the projected representation, scales them with a sample-dependent coefficient, and supplies the corrected representation to the language model.Predictive uncertainty determines the correction strength, while the visual encoder and language model remain unchanged.Source: §4.1 · pp. 4–5

4.2 Target Representation and Residual Construction

  • Verified captions are tokenized into target representations, and the estimator learns the residual from the original projection so verified targets are unnecessary at inference.The projected representation has 96 positions, so the first 96 target-caption embeddings are retained.Source: §4.2 · p. 5

4.3 Proposed Low-Rank Sparse Variational Gaussian Process with Natural-Parameter Updates

  • The sparse variational GP uses a low-rank inducing-covariance approximation consistently through inference, with natural-parameter updates.The approximation retains dominant eigencomponents and avoids explicitly inverting the full-rank covariance.Source: §4.3 · pp. 5–7

4.4 Proposed Combined Kernel

  • Partitioning the flattened representation and combining separate kernel components improved residual-prediction accuracy over applying one kernel to the full feature vector.Each component uses a linear kernel, allowing segments to contribute separately while retaining the sparse-GP computational structure.Source: §4.4 · pp. 7–8

4.5 Uncertainty-Aware Correction: Alpha formulation

  • A sample-specific α_i uses predictive uncertainty and predictive-mean variation to scale the residual, but residual-prediction errors may still affect the corrected representation.The coefficient uses inference-time predictive statistics; the verified target representation and ground-truth residual are not required at test time.Source: §4.5 · p. 9

4.6 Integration with VideoChat2

  • The trained estimator predicts a residual and computes α_i before the corrected representation replaces the original input to Phi-3 Mini; the remaining VideoChat2 inference components stay unchanged.The correction module is placed between multimodal projection and language generation.Source: §4.6 · pp. 9–10

5 Experiments

The experiments evaluate residual prediction and caption correction on real-world low-light ARID videos, using a separately trained correction module while keeping VideoChat2 fixed. Overall residual metrics are reported alongside per-action results, residual statistics, inference-cost measurement, and qualitative caption comparisons.

5.1 Dataset and Experimental Protocol

  • The ARID v1.5 protocol uses 44 videos for training, 11 for validation, and the remainder for testing, with verified action-caption targets.The 44 training videos comprise four videos from each of 11 action classes; target-caption embeddings provide representation-level residual targets.Source: §5.1 · p. 10

5.2 Implementation Details

  • The correction model processes a flattened 294,912-dimensional projection using a sparse Gaussian process with 22 inducing points and nine feature segments.Each segment uses a linear kernel, whose components are summed; the model is trained for 20 epochs while the underlying VideoChat2 parameters remain fixed.Source: §5.2 · p. 10

5.3 Evaluation Metrics

  • Residual prediction is assessed with MSE, MAE, and NLPD, alongside comparisons of predicted and target residual means and standard deviations.NLPD accounts for predictive uncertainty, while the mean and standard-deviation comparison checks whether predicted residuals share target residuals’ statistical characteristics.Source: §5.3 · p. 11

5.4 Inference Efficiency

  • Test-time runtime includes sparse-GP prediction, correction-coefficient computation, corrected-representation formation, and subsequent Phi-3 Mini caption generation.The measurement reflects the additional inference procedure while VideoChat2 parameters remain fixed.Source: §5.4 · p. 11

5.5 Caption-Level Evaluation

  • Caption-level evaluation compares original and corrected outputs against each video’s visual content, focusing on whether the described human action is more accurate.A correction may count as beneficial when it reduces an original error, even if minor inaccuracies remain, provided the corrected caption is substantially more consistent with the video.Source: §5.5 · p. 11

5.6 Results

  • MSE 0.0277, MAE 0.1045, and NLPD −0.0934 summarize overall residual-estimation performance across test samples.Sit had the lowest MSE and MAE (0.0138 and 0.0753), while Wave had the highest (0.0380 and 0.1212); target and predicted residual means were −0.00272 and −0.00262, with standard deviations 0.58518 and 0.57423.Source: §5.6 · p. 11, Table 1 · p. 12, Table 2 · p. 12

5.7 Qualitative Caption Correction

  • Qualitative examples show corrected representations replacing incorrect or missing actions with descriptions closer to the observed video content.The examples are selected to illustrate reduced semantic errors or recovered information in action descriptions.Source: §5.7 · p. 12, Table 4 · p. 12

6 Conclusion

The paper presents an uncertainty-aware correction module that refines a VLM’s intermediate representation without fine-tuning its backbone. On low-light ARID videos, experiments show residual prediction and improvements in action-related caption content.

6 Conclusion

  • The correction module refines VideoChat2’s intermediate representation before language generation, leaving the underlying model unmodified.It predicts a residual from the original projection to a verified target representation, then scales and adds that residual without requiring the target representation at inference.Source: §6 · p. 13
  • A sparse Gaussian process with a partitioned combined-kernel design estimates residuals, while predictive uncertainty adjusts correction strength per test sample.The inducing-point formulation and separate correction architecture are presented as supporting a small training set and low test-time overhead.Source: §6 · p. 13
  • On ARID low-light videos, results show small residual-prediction errors, similar target and predicted residual statistics, and qualitative improvements to incorrect or missing action descriptions.The conclusion presents these findings as supporting the feasibility of uncertainty-aware representation correction without fine-tuning the full VLM.Source: §6 · p. 13

Appendix

The appendix contains before-and-after caption examples for low-light videos across several action categories.

Appendix

  • For Drink, the original caption describes smoking, while the revised caption describes holding and sipping wine; for Jump, a still pose becomes a dance description.These are examples of differing action descriptions, not quantitative comparisons.Source: Appendix · p. 14
  • For Wave, the original caption describes a throw, while the revised text describes gestures; for Pour, sipping coffee becomes pouring liquid into a cup.Source: Appendix · p. 14
  • For Turn, the original caption describes a stationary man, while the revised caption describes him turning and walking; the Sit example shifts from a specific scene to a general seated description.The Sit captions contrast a woman in a dark room with a person described as seated with crossed legs.Source: Appendix · pp. 14–15
  • The Stand example changes a stationary man into someone rising from a seated position, while the Push example changes skateboard riding into lawn-mower pushing.Source: Appendix · p. 15

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