Video Attention Score (VAS)

• Video Attention Score (VAS) is a metric defining either learned spatial-temporal attention weights or a normalized, LLM-based dataset score for assessing visual grounding in videos.
• Methodologies using VAS extract CNN frame features and compute temporal and multi-hop spatial weights or LLM-judged reasoning scores to generate robust video descriptors.
• Empirical results indicate that VAS improves person re-identification accuracy and offers interpretability in video reasoning, despite challenges in differentiating claimed versus actual visual evidence.

Video Attention Score (VAS) refers to distinct concepts and metrics developed for video understanding tasks, typically in the domains of person re-identification and multimodal video reasoning. In some works, VAS denotes learned frame-level or spatial-temporal attention weights that modulate feature aggregation for robust video descriptors. In others, VAS is defined as a dataset-level metric quantifying the extent to which multimodal reasoning is grounded in visual evidence, assessed algorithmically by LLMs. The following sections elucidate formal definitions, architectures, computational protocols, empirical findings, and comparative analysis of VAS across representative research.

1. Formal Definitions and Conceptual Distinctions

VAS encompasses two principal forms in the literature:

1. Attention Weight-Based VAS: In spatial-temporal attention networks for person re-identification, VAS denotes per-frame ($\alpha_i^t$) and per-frame-per-hop spatial ($\alpha_i^{s_j}$) attention scores. Temporal attention scores $\alpha_i^t \in (0,1)$ quantify the informativeness of each frame, while spatial attention scores $\alpha_i^{s_j} \in (0,1)$ select salient regions within frames over multiple hops (Rao et al., 2018).
2. Text-Based VAS Metric: In the context of multimodal reasoning and video question-answering (Video-R2), VAS is the mean normalized score, assigned by a large LLM judge, reflecting how much a model’s explicit reasoning text is grounded in direct visual evidence from the video. Each reasoning trace is scored integer $r_i \in \{0,\ldots,10\}$ and normalized as $s_i = r_i / 10$. The aggregate $\mathrm{VAS}(M, D)$ is computed as $\frac{1}{|D|} \sum_{i=1}^{|D|} s_i$, where $M$ is a model and $D$ a dataset (Maaz et al., 28 Nov 2025).

2. Attention Weight Computation for Video Representation

In spatial-temporal attention networks, VAS is operationalized via direct computation from learned representations:

• Frame-Level CNN Extraction: Each frame is processed by a shared CNN to yield $x_i \in \mathbb{R}^{128}$.
• Temporal Attention: A weight vector $\theta$ linearly projects frame features and applies an element-wise sigmoid:

$\alpha_i^t = \sigma(\theta^T x_i)$

• Spatial Attention (Multi-Hop): For each hop $j=1…J$, a 2D convolution ($5 \times 5$ kernel) followed by sigmoid yields:

$$\alpha_i^{s_j} = \sigma(\operatorname{conv}_j(x_i))$$

• Aggregation: Weighted sum produces both temporal and spatial feature vectors, which are then combined across hops to yield the final video descriptor:

$$\begin{align*} f^t &= \sum_{i=1}^T \alpha_i^t x_i \ f^s_j &= \sum_{i=1}^T \alpha_i^{s_j} x_i \ F_j &= f^t + f^s_j \ F &= \sum_{j=1}^J F_j \end{align*}$$

These scores are learned end-to-end with identification and hinge losses, enabling the model to down-weight uninformative or occluded frames and regions (Rao et al., 2018).

3. Dataset-Level VAS Metric for Multimodal Reasoning

In video reasoning benchmarks, VAS is constructed as follows:

• Prompted Reasoning Generation: The model $M$ is required to emit a > … chain-of-thought for each video-question pair.
• LLM Judge Evaluation: A large LLM (default: Qwen3-Next-80B-A3B) is used to score the reasoning based on a rubric reflecting density and specificity of visual evidence referenced. The output is a JSON containing a raw score $r$ and justification.
• Normalization and Aggregation: Each score $r_i$ is scaled to $s_i$; the dataset VAS is the mean over all examples.
• Interpretation: High VAS indicates frequent and explicit reliance on concrete video details (objects, actions, timestamps), while low values reflect generic, text-prior-dominated reasoning (Maaz et al., 28 Nov 2025).

Pseudocode for VAS evaluation:

def compute_VAS(model, dataset): total_score = 0.0 for x in dataset: reasoning_text = model.generate(x.video + x.question, enforce_think_block=True) judge_output = LLM_judge(reasoning_text) r = judge_output["score"] # 0–10 s = r / 10.0 total_score += s return total_score / len(dataset)

4. Hyperparameters, Architectural Choices, and Optimization

Attention Networks for Person Re-ID (Rao et al., 2018)

• Network: Frame-Level CNN (56×40, 5 channels), three Conv→MaxPool→Tanh blocks, final 128-D FC.
• Attention: Temporal attention (one linear $\theta$ per frame); spatial attention (three 5×5 conv hops, $J=3$).
• Training: SGD, learning rate $1e^{-4}$, batch size $1$, $1100$ epochs, hinge loss (margin $m=2$), random cropping, mirroring, sequence sampling $T=16$.
• Aggregation: Temporal and multi-hop spatial weights combined for discriminative representation.

VAS Metric for Video Reasoning (Maaz et al., 28 Nov 2025)

• LLM Judge: Qwen3-Next-80B-A3B (default) with reproducible scoring rubric; alternative judges tested for stability.
• Benchmarking: 11 diverse video QA datasets; models forced to emit explicit reasoning.
• Reporting: VAS, accuracy, Think Answer Consistency (TAC) presented via heatmaps and bar plots.

5. Empirical Results and Interpretability

Person Re-ID Networks (Rao et al., 2018)

• Performance:
  • iLIDS-VID: Rank-1 accuracy 66.0% (exceeding prior work by Xu et al. and Zhou et al.).
  • PRID-2011: Rank-1 accuracy 88.0%.
  • Ablation: Increasing spatial hops $J$ from $0$ to $3$ yields monotonic gain (Rank-1: 60% to 66%).
• Qualitative Insights:
  • Retrieval grids show correct down-weighting of occluded/low-quality frames.
  • Failure cases occur when identities exhibit high appearance/motion similarity.

Video Reasoning VAS Metric (Maaz et al., 28 Nov 2025)

• Video-R2 Model: Achieves VAS ≈ 0.69, outperforming VideoRFT and prior baselines.
• Benchmark Split: Higher VAS for reasoning-intensive tasks (≈0.78) than generic (≈0.63).
• Stability: LLM judge selection robust (Pearson $r > 0.7$ across variants).
• Limitations: VAS measures "claimed" visual grounding in reasoning text, not actual exploitation of video features.

Model	VAS Overall	VAS (Reasoning)	VAS (Generic)
Video-R1	0.53	—	—
VideoChat-R1	0.49–0.51	—	—
VideoRFT	0.61	—	—
Video-R2	0.69	0.78	0.63

Editor's term: “claimed grounding” denotes what is attested in generated reasoning, not necessarily verifiable from underlying attention maps or latent features.

6. Comparison and Methodological Analysis

• Internal Attention vs. Text-Based VAS: Model-intrinsic attention aggregation (e.g., attention-weight summing across visual tokens) may be miscalibrated and fail to align with actual textual references to visual signals. The text-based VAS metric provides interpretability, cross-model comparability, and independence from model architecture.
• Semantic Consistency Rewards: Embedding-based consistency measures (e.g., SigLIP in VideoRFT) capture coarse alignment between modality embeddings, but do not directly assess explicit mention of visual evidence in reasoning.
• Composite Metrics: VAS is typically used alongside accuracy and consistency metrics (TAC) to produce a more holistic evaluation of multimodal models.

7. Limitations and Future Directions

VAS, in both attention-weight and text-based variants, is subject to limitations:

• In attention-weight networks, irrelevant frames may still receive nonzero scores, especially when appearance/motion ambiguity is high.
• Text-based VAS can be artificially inflated if models fabricate plausible but ungrounded visual details.
• Future approaches may integrate both latent attention signal analysis and external LLM judgment to triangulate actual versus claimed visual reasoning.

*This comprehensive treatment summarizes current definitions and operationalizations of Video Attention Score (VAS) in video understanding research, referencing leading works (Rao et al., 2018, Maaz et al., 28 Nov 2025).