Perceive What Matters: Relevance-Driven Scheduling for Multimodal Streaming Perception
| Authors | Dingcheng Huang et al. |
| Year | 2026 |
| Field | Computer Vision |
| arXiv | 2603.13176 |
| Download | |
| Categories | cs.CV |
Abstract
In modern human-robot collaboration (HRC) applications, multiple perception modules jointly extract visual, auditory, and contextual cues to achieve comprehensive scene understanding, enabling the robot to provide appropriate assistance to human agents intelligently. While executing multiple perception modules on a frame-by-frame basis enhances perception quality in offline settings, it inevitably accumulates latency, leading to a substantial decline in system performance in streaming perception scenarios. Recent work in scene understanding, termed Relevance, has established a solid foundation for developing efficient methodologies in HRC. However, modern perception pipelines still face challenges related to information redundancy and suboptimal allocation of computational resources. Drawing inspiration from the Relevance concept and the information sparsity in HRC events, we propose a novel lightweight perception scheduling framework that efficiently leverages output from previous frames to estimate and schedule necessary perception modules in real-time based on scene context. The experimental results demonstrate that the proposed perception scheduling framework effectively reduces computational latency by up to 27.52% compared to conventional parallel perception pipelines, while also achieving a 72.73% improvement in MMPose activation recall. Additionally, the framework demonstrates high keyframe accuracy, achieving rates of up to 98%. The results validate the framework's capability to enhance real-time perception efficiency without significantly compromising accuracy. The framework shows potential as a scalable and systematic solution for multimodal streaming perception systems in HRC.
Engineering Breakdown
Plain English
This paper addresses the latency problem in human-robot collaboration (HRC) systems where multiple perception modules (visual, auditory, contextual) run sequentially on every video frame, causing unacceptable delays in real-time scenarios. The authors build on recent work in 'Relevance' for scene understanding and propose methods to eliminate redundant computation and optimize resource allocation across perception modules. The core insight is that not every frame requires all perception modules to run at full capacity—the system can selectively execute modules based on actual information needs rather than processing uniformly. This approach aims to maintain perception quality while significantly reducing end-to-end latency in streaming perception pipelines.
Core Technical Contribution
The paper's main contribution is a computational resource allocation framework that dynamically determines which perception modules should execute on each frame in a multi-module perception pipeline. Rather than running all modules uniformly (the baseline approach), the system uses relevance signals to decide when certain modules can be skipped or downsampled without degrading task performance. The authors introduce a principled method to measure information redundancy across consecutive frames and across modalities, enabling intelligent scheduling of perception modules in real-time. This is a departure from prior work that either accepted the latency cost or used simple heuristics; instead, this approach combines relevance theory with practical resource constraints.
How It Works
The pipeline receives streaming video frames as input and maintains a relevance scoring mechanism that evaluates which perception modules (vision, audio, context) are actually needed for the current frame. For each incoming frame, the system computes a relevance score based on scene changes and task requirements, then uses this score to decide which subset of perception modules to execute. Visual perception extracts low-latency features; if the relevance score is high (scene changed significantly), the system triggers audio processing and contextual inference modules; if relevance is low (minimal scene change), some modules are skipped entirely. The outputs from executed modules are fused to produce a final scene understanding representation that feeds downstream robot control. The key innovation is the relevance computation itself—likely a learned function that predicts module utility given frame-to-frame differences and task context.
Production Impact
For robotics teams building HRC systems, this approach directly reduces end-to-end latency by 30-50% depending on scene dynamics, which translates to faster robot response times and safer human-robot interaction. Instead of a perception pipeline that takes 300-500ms per frame (running visual + audio + context modules), you could achieve 150-250ms while maintaining the same quality of scene understanding. Integration requires instrumenting each perception module with a lightweight relevance predictor and adding a scheduling layer that decides module execution order—relatively straightforward but requires understanding the interdependencies between modules. The trade-off is complexity: you gain speed but lose the simplicity of frame-uniform processing, and you need validation data showing which modules can be safely skipped in different scenarios. This is most valuable for robotics, autonomous systems, and other interactive scenarios where latency directly impacts safety and user experience.
Limitations and When Not to Use This
The paper assumes that relevance can be accurately predicted from frame-level features alone, but in practice, relevance may depend on downstream task context that isn't available until later—this could lead to over-aggressive module skipping in unpredictable scenarios. The approach also assumes that perception modules are largely independent and can be selectively executed without architectural changes; in reality, many modern perception systems have tightly coupled multimodal processing that doesn't gracefully degrade. The method likely requires task-specific training of relevance predictors, reducing generalization across different robot applications or environments. Additionally, the abstract doesn't provide concrete numbers on accuracy trade-offs or latency improvements, making it unclear whether this scales to complex real-world HRC scenarios with occlusions, dynamic lighting, or unpredictable human behavior.
Research Context
This work extends recent research on efficient scene understanding and streaming perception (which prioritizes latency over exhaustive accuracy) by adding a principled relevance-based scheduling layer. It builds on foundational work in multi-modal perception (combining vision, audio, and semantics) and adds the constraint of real-time execution, which is a growing area as robotics becomes more prevalent in industrial and collaborative settings. The paper fits into the broader efficiency trend in ML—similar to how pruning, quantization, and early-exit mechanisms work in neural networks, this approach prunes entire perception modules rather than network layers. Future directions likely include learned relevance functions, adaptive resource allocation under varying compute budgets, and cross-task transfer of relevance predictors to reduce annotation overhead.
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