Onscreen/Offscreen Event Classification

• Onscreen/offscreen event classification is the systematic differentiation between events directly captured within a sensor’s field-of-view and those inferred from ambient cues.
• It leverages multimodal integration techniques, including audiovisual fusion, transformer-based audio generation, and sensor-based tap detection to enhance event interpretation.
• Robust evaluation metrics and adaptive methodologies validate its performance in practical applications like video editing, mobile interaction, and context-aware computing.

Onscreen/Offscreen Event Classification refers to the systematic identification, discrimination, and labeling of events in data streams—whether visual, audio, text, or sensor-based—as either occurring within (“onscreen”) or outside (“offscreen”) the domain of direct observation by the sensing system. This distinction plays a pivotal role in artificial intelligence and multimodal understanding, underpinning advances in event localization, scene analysis, mobile interaction, audiovisual fusion, and compressed domain event processing. The following sections survey core methodologies, technical realizations, evaluation protocols, and application contexts as documented in recent academic work.

1. Definitions and Contexts

Onscreen events denote phenomena that are directly detectable or visible to an acquisition system (e.g., the field-of-view of a camera, the visual frame of a video, the spatial region of an audio perspective, or the interactive area of a touchscreen device). Conversely, offscreen events are not directly perceived but may be inferred from contextual, semantic, or ambient cues—such as sounds generated by objects outside a camera’s frame, touches on non-display facets, or textual events alluded to but not depicted.

Classification along this dimension is crucial for accurate analysis of multimodal data where the boundary between observability and inference affects the downstream interpretation, system responsiveness, and user interface logic (Shimada et al., 16 Jul 2025, Kushwaha et al., 14 Dec 2024).

2. Methodologies and Architectures

a. Audiovisual Event Detection in Stereo SELD

In stereo sound event localization and detection (SELD), the distinction between onscreen and offscreen events arises due to the limited field-of-view (FOV) of perspective video. The DCASE2025 Task3 framework converts Ambisonic audio and 360° video into stereo audio and 100° FOV video clips. For every time segment, each sound event is assigned a label: onscreen if its estimated direction-of-arrival (DOA) falls within the video FOV, offscreen otherwise. A multi-ACCDOA output CRNN architecture processes audio features, while cross-attention fusion layers enable audiovisual integration. Onscreen/offscreen event classification is implemented through binary output neurons, trained using binary cross-entropy loss alongside standard event detection and localization objectives (Shimada et al., 16 Jul 2025).

b. Holistic Audio Generation

Holistic audio generation, as approached in VinTAGe, addresses the limitation that video-to-audio (V2A) models excel at onscreen synchronization but neglect offscreen semantics, while text-to-audio (T2A) systems provide semantic completeness at the cost of temporal alignment. VinTAGe employs a flow-based transformer (‘Joint VT-SiT’) that jointly conditions audio generation on temporally-aligned video features (CLIP + optical flow) and semantically-rich text embeddings (FLAN-T5). Gated cross-attention modules integrate these cues, while classifier-free guidance and teacher-alignment losses mitigate modality bias, ensuring onscreen (e.g., visible actions) and offscreen (e.g., unseen ambient noise) components are both captured in the synthesized audio (Kushwaha et al., 14 Dec 2024).

c. Tap Classification for Off-Screen Mobile Input

TapNet frames onscreen (capacitive touch) and offscreen (e.g., back tap, edge tap) event detection as a multi-task learning problem using IMU signals from smartphones. The model is a one-channel convolutional network that fuses accelerometer and gyroscope data with auxiliary device information, enabling simultaneous classification of tap location, direction, and finger part. Offscreen taps, confirmed via IMU peak gating and distinguished from onscreen events by their location and force signature, can trigger system actions even when the touchscreen is inaccessible (Huang et al., 2021).

d. Event-Based Vision: Coding and Subsampling

For event camera streams, classification tasks often require distinguishing events generated by moving objects onscreen from those caused by peripheral, ambient, or sensor noise stimuli offscreen. Double deep learning-based architectures encode asynchronous spatiotemporal event tuples $(x, y, t, p)$ as 3D point clouds, facilitating compression and robust handling of lossy coding (e.g., via JPEG PCC). Subsampling methods, especially those based on causal density (where event density is computed using a spatiotemporal filter over past events), preferentially retain events in high-density/onscreen regions. This preserves task-relevant, onscreen information in scenarios with severe bandwidth or computation constraints (Seleem et al., 22 Jul 2024, Araghi et al., 27 May 2025).

e. Contextual Event Reasoning in Text

ClarET generalizes the onscreen/offscreen distinction to language and narrative domains by pre-training a context-to-event Transformer to exploit event-centric correlations (whole event recovery, contrastive encoding, prompt-based locating). The model, though not explicit about onscreen/offscreen categories, learns to differentiate explicitly described events (onscreen) from those inferred through context (offscreen) by building representations that capture causal, temporal, and contrast relationships between events within a passage (Zhou et al., 2022).

3. Technical Protocols and Algorithms

Method/Task	Key Feature/Architecture	Onscreen/Offscreen Discrimination Mechanism
Stereo SELD (Shimada et al., 16 Jul 2025)	CRNN + Transformer fusion	DOA within/outside FOV after audio-visual fusion
VinTAGe (Kushwaha et al., 14 Dec 2024)	Flow-based Transformer	Joint VT gated attention, teacher-guided loss
TapNet (Huang et al., 2021)	1D CNN + device vector	Location/direction regression, IMU gating
Event Camera Coding (Seleem et al., 22 Jul 2024)	DL point cloud coding	Spatial/temporal (x, y, t) density & coding pathway
Causal Subsampling (Araghi et al., 27 May 2025)	Density-based filter	Spatiotemportal density, prioritized event selection
ClarET (Zhou et al., 2022)	Enc-Dec Transformer	Event-correlation objectives, embedding contrast

Specifically, stereo SELD uses mathematically defined projection (rotation of DOA) to ascertain onscreen/offscreen status, whereas TapNet exploits spatial tap localization (e.g., grid cell classification, MAE for tap regression), and event camera frameworks use thresholded density scores $f_i^{(p)}$ based on Gaussian spatial and exponential temporal windows.

4. Evaluation Metrics and Performance

Onscreen/offscreen classification tasks are quantitatively assessed using domain-specific metrics:

• Stereo SELD: Macro $F_{20^\circ/1/\text{onoff}}$—accuracy across class, spatial, and onscreen/offscreen output; DOAE and RDE for precise localization. Reported onscreen/offscreen accuracies reach ~80% on development and ~77.8% on evaluation (Shimada et al., 16 Jul 2025).
• VinTAGe: Evaluates generation quality (FAD, FID), audio-visual alignment (cosine similarity in embedding space), and use of pretrained classifiers for event type and location (onscreen/offscreen) labeling on purpose-built benchmarks (Kushwaha et al., 14 Dec 2024).
• TapNet: Weighted F1 scores for event type/location, MAE for regression, with specific gains (e.g., 161.5% improvement in tap location classification vs. prior art) for offscreen input (Huang et al., 2021).
• Event Camera Subsampling: Normalized area-under-the-curve (nAUC) for classification over varying event counts, demonstrating that causal density-based filtering achieves near-original model accuracy even under severe bandwidth reduction (Araghi et al., 27 May 2025).

In all cases, the evaluation frameworks emphasize joint accuracy in event type identification, spatial/temporal localization, and modality-specific partitioning (onscreen/offscreen) as core criteria.

5. Application Scenarios and Limitations

Onscreen/offscreen classification supports a broad range of real-world use cases:

• Audiovisual: Automated video editing, dynamic audio spatialization, context-aware hearing aids, and camera steering depend on accurate onscreen/offscreen audio-visual event detection (Shimada et al., 16 Jul 2025, Kushwaha et al., 14 Dec 2024).
• Mobile Interaction: Offscreen taps enable assistive or accessibility features for users unable to interact directly with touchscreens, while also facilitating background interactions (e.g., wallpaper shortcuts) (Huang et al., 2021).
• Vision and Data Transmission: Edge AI devices using event cameras benefit from subsampling and compression strategies that prioritize onscreen events, optimizing for transmission and real-time processing (Seleem et al., 22 Jul 2024, Araghi et al., 27 May 2025).
• Textual Reasoning: In narrative understanding, distinguishing explicit (onscreen) versus inferred (offscreen) events improves coherence judgments, question answering, and story completion in NLP systems (Zhou et al., 2022).

However, challenges persist. Onscreen/offscreen discrimination suffers from dataset bias (e.g., overrepresentation of offscreen events), limited spatial resolution in stereo or event sensing, and modality bias in joint fusion models. In stereo SELD, models underutilize the visual stream, yielding onscreen/offscreen classification metrics only marginally above naive baselines. In event camera processing, fixed thresholding can fail in highly sparse regimes, necessitating adaptive methods.

6. Future Directions

Ongoing research aims to address remaining barriers:

• Audiovisual Fusion: Advanced transformer architectures to more effectively align audio-visual streams, exploiting optical flow or multi-modal cross-attention for robust onscreen/offscreen mapping (Shimada et al., 16 Jul 2025, Kushwaha et al., 14 Dec 2024).
• Personalization and Device Adaptation: Improving cross-device transfer and personalized inference in mobile and wearable sensing, including unsupervised/few-shot adaptation for offscreen tap and gesture recognition (Huang et al., 2021).
• Adaptive Subsampling: Threshold normalization and density-adaptive selection to ensure stable performance across heterogeneous event rates and device configurations (Araghi et al., 27 May 2025).
• Benchmarking and Datasets: Creation of large-scale, annotation-rich datasets (e.g., VinTAGe-Bench) specifically designed to support fine-grained onscreen/offscreen event classification and generation (Kushwaha et al., 14 Dec 2024, Shimada et al., 16 Jul 2025).
• Semantic Generalization: Leveraging event correlation objectives and prompt-based reasoning to generalize onscreen/offscreen cues across modalities, particularly in abstraction-rich NLP tasks (Zhou et al., 2022).

7. Summary Table: Methods and Target Domains

Paper/Approach	Sensing Modality	Main Application Domain	Onscreen/Offscreen Mechanism
Stereo SELD (Shimada et al., 16 Jul 2025)	Audio/Video	Sound event localization, detection	DOA/fov projection, cross-attention
VinTAGe (Kushwaha et al., 14 Dec 2024)	Audio/Video/Text	Audio generation	Multimodal transformer, teacher loss
TapNet (Huang et al., 2021)	IMU	Mobile input, interaction	Location grid, force/direction vectors
Event Camera Coding (Seleem et al., 22 Jul 2024)	Event camera	Vision/classification, compression	3D point cloud, density threshold
ClarET (Zhou et al., 2022)	Text/Narrative	Commonsense/event reasoning	Contrastive embedding, context cues

In conclusion, onscreen/offscreen event classification forms a critical axis for modern multimodal AI research and deployment, guiding the design of architectures, datasets, and evaluation strategies across audio, video, sensor, and textual data modalities. Methods that leverage joint modeling, context-sensitive representation, and adaptive filtering yield state-of-the-art performance and point toward future systems capable of nuanced event understanding across the visible and inferred domains.