Early-Exit Strategies for Dynamic Graph CNNs to Accelerate Inference of Point Clouds
Bibliographic record
Abstract
Dynamic Graph Convolutional Neural Networks (DGCNNs) are effective for processing graph-structured data, particularly point clouds used in immersive media, augmented reality (AR), and virtual reality (VR) applications. However, their inference can be computationally intensive, especially with high-resolution inputs, posing challenges for latency-sensitive and resource-constrained deployments. To address these challenges, we propose confidence-based early exit architectures tailored for fast inference in multimedia-oriented DGCNN systems. The proposed method accelerates inference by allowing samples to exit at intermediate layers once a confidence threshold is met, while preserving or even enhancing classification accuracy. Our best-performing configuration achieves a 3× speed-up alongside a 0.59% accuracy gain, while the most efficient design provides a 4.3× speed-up with only a minor accuracy trade-off. These results demonstrate the potential of early exiting as a lightweight and scalable solution for point-cloud inference in future multimedia communication systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".