VIP CUP 2025 Dataset

• VIP CUP 2025 dataset is a multimodal, annotation-rich benchmark combining video, image, and aerial data with task-specific labels for advanced vision tasks.
• It features rigorous design principles with diverse modalities—including RGB, infrared, and semantically annotated keyframes—and employs cosine similarity-based keyframe pruning.
• Benchmark outcomes reveal strong performance in video infilling, deepfake detection, and drone-bird discrimination, guiding future multimodal fusion research.

The VIP CUP 2025 Dataset is a collection of multimodal, annotation-rich datasets designed for advanced benchmarking in video, image, and aerial object reasoning. It is the centerpiece for evaluating chain-of-thought modeling, deepfake detection, and robust drone-bird discrimination under adverse conditions. The dataset features carefully curated splits, diverse modalities (including RGB, infrared, and semantically described video keyframes), and task-specific labeling conventions that enable quantitative assessment across several cutting-edge computer vision and reasoning tasks.

1. Dataset Design Principles and Composition

VIP CUP 2025 encompasses distinct data resources for multiple competition tracks, each with strict design criteria to enable rigorous evaluation:

• Video Reasoning Track (VIP Dataset): Centred on sparse keyframe selection from footage (predominantly YouTube-8M), using a hybrid of semantic and visual embeddings (CLIP, Detic) with iterative pruning based on cosine similarity. Each selected keyframe receives two forms of textual annotation:
  • Unstructured Dense Captions: Detailed natural language sentences capturing objects, actions, mood, and setting.
  • FAMOuS Structured Descriptions: Decomposed labels specifying Focus, Action, Mood, Objects, and Setting, inspired by descriptive scene plays.
• Deepfake Detection Track (DFWild-Cup Dataset): Aggregates images from eight well-known deepfake corpora. The training split manifests strong class imbalance: approximately 42,690 real and 219,470 fake images. Validation sets are nearly balanced (around 1,500 samples per class), anonymized and standardized with modality-conformant resizing.
• Aerial Object Detection Track: Features paired RGB and infrared images (45,000 training, 6,500 validation) with pixel-level registration. All images are annotated in YOLO format, focusing on binary classification between birds and drones under various environmental and imaging distortions (fog, blur, uneven illumination).

The dataset supports advanced preprocessing, including noise reduction (adaptive median filtering), blur correction (Richardson–Lucy deconvolution), and structure enhancement (unsharp masking), in addition to diverse data augmentation pipelines.

2. Keyframe Extraction, Annotation, and Semantic Structuring

The video reasoning track is defined by its efficient reduction of high-dimensional video input to semantically meaningful sparse sets:

• Keyframe Pruning Algorithm: For candidate frames $i_j$, $t_j$ (image and object-embedding vectors), adjacency-based scores are calculated:

$$\mathrm{score}[j] = \mathrm{mean}\left(\mathrm{cos}(t_j, t_{j-1}, t_{j+1}),\, \mathrm{cos}(i_j, i_{j-1}, i_{j+1})\right)$$

The frame $s^*$ with highest mean similarity to its neighbors is discarded, iteratively minimizing redundancy while retaining essential transitions.

• Annotation Schema: Each retained frame receives:
  • Unstructured caption: linguistically rich, context-heavy description.
  • FAMOuS annotation: segmented into precisely defined labels for focus (primary subject), actions, evoked mood, enumerated objects, and contextual setting, enabling interpretable multi-faceted chain-of-thought reasoning.

This structuring allows for rigorous evaluation of video reasoning as sequential, language-like inference, rather than mere frame-wise classification.

3. Benchmark Tasks and Evaluation Protocols

VIP CUP 2025 prescribes two distinct reasoning tasks:

• Video Infilling: Given a sequence $k_1, \dots, k_n$ with blocks $k_i, \dots, k_j$ masked, models receive context from $k_{i-n}, \dots, k_{i-1}$ and $k_{j+1}, \dots, k_{j+n}$, outputting the missing frame descriptions (either unstructured $u_{i \dots j}$ or structured $s_{i \dots j}$).
• Video Prediction: Provided context $u_{i-n}, \dots, u_i$ or $s_{i-n}, \dots, s_i$, models predict the next $f$ frames $u_{i+1 \dots i+f}$ or $s_{i+1 \dots i+f}$. Increasing prediction spans induce exponential growth in possibility space given unidirectional context.
• Image Classification and Detection: Deepfake detection and drone-bird discrimination tracks feature cross-domain benchmarking, including:
  • Precision, average precision ($mAP_{50}$, $mAP_{50-95}$), F1-score for object detection.
  • ROC curve area, equal error rate (EER), and t-SNE/Grad-CAM visualizations for deepfake clustering.

Models benchmarked include leading LLMs (GPT-4, GPT-3, Vicuna) for language-driven video reasoning, ensemble CNN-transformer architectures (CAE-Net, EfficientNet, DeiT, ConvNeXt), and lightweight detection frameworks integrating Ghost modules, deformable convolutions, and attention layers (EGD-YOLOv8n).

4. Modalities, Augmentation Strategies, and Domain Fusion

The dataset showcases advanced multimodal fusion techniques:

• Image Pairing: Each RGB image (3-channels) is aligned and concatenated with a corresponding IR image (1-channel) to support four-channel network input, enabling direct, cross-modal feature learning.
• Augmentation Schemes: Training employs random horizontal flips, rotations, brightness/contrast perturbations, and mixup. For frequency-domain branches (e.g., XceptionNet with Haar DWT), spatial augmentations are minimized to protect spectral integrity.
• Attention and Efficiency Features:
  • GhostConv and C3Ghost modules reduce parameterization cost while preserving expressiveness.
  • Efficient Multi-Scale Attention (EMA) mechanisms operate jointly on channel and spatial dimensions:

  $$\mathrm{EMA}(X) = \mathrm{Softmax}(W_{\mathrm{channel}} X) \odot \mathrm{Softmax}(W_{\mathrm{spatial}} X)$$ - Deformable convolutional detection heads dynamically adapt kernels to non-rigid object geometries:

  $y(p_0) = \sum_p w(p) \cdot x(p_0 + p + \Delta p)$

5. Benchmarking Outcomes, Strengths, and Limitations

Empirical analyses across VIP CUP 2025’s tracks yield:

• Video Reasoning: Dense captions outperform structured FAMOuS on word-overlap metrics (ROUGE$_l$), while FAMOuS breakdowns reveal component-level weaknesses (e.g., “Objects” easier to infer than “Action” or “Setting”). Models fare better on infilling (bidirectional context) than prediction (unidirectional context).
• Deepfake Detection: CAE-Net ensemble achieves validation accuracy of 94.63%, EER of 4.72%, AUROC of 97.37%. Visualization (via Grad-CAM/t-SNE) demonstrates high separability despite dataset imbalance. Robustness against adversarial perturbations is inherent to model diversity, though not empirically quantified.
• Drone-Bird Detection: Multimodal fusion model (EGD-YOLOv8n) achieves precision $\sim$0.901, mAP improvements of 9–21% over baseline YOLOv8n, with real-time inference rates ($>$54 FPS on Tesla T4 GPU).

Limitations observed include low absolute accuracy in multi-hop video reasoning (indicating substantive open challenges), and requirement for further exploration in adversarial sample resilience.

6. Scientific Impact and Future Directions

The VIP CUP 2025 dataset establishes a new paradigm for chain-of-thought video reasoning, multimodal fusion benchmarking, and practical object detection under adverse real-world conditions. Its design foregrounds computational efficiency, interpretability through language annotation, and modality integration—driving research in:

• Language-based video modeling and structured chain-of-thought inference.
• Robust cross-domain generalization for synthetic media detection and security.
• Lightweight, fused multimodal architectures suitable for deployment in resource-constrained environments.

A plausible implication is that continued refinement of multimodal annotation frameworks and augmentation pipelines will stimulate advances in both theory (temporal/chain-of-thought inference, multimodal learning) and application (autonomous surveillance, synthetic media forensics, hybrid video understanding). Real-world deployment will benefit from persistent research into adversarial robustness and explainability at the intersection of vision, language, and domain fusion.