Peer Learning Approach to Unbiased Scene Graph Generation for Traffic Scene Understanding
Bibliographic record
Abstract
The biased scene graph generation problem arises from the inherent long-tailed distributions of predicates, which are challenging to handle effectively with a single network. In this paper, we introduce a novel framework called peer learning, designed to address the issue of unbiased scene graph generation (USGG) through a divide-and-vote approach. To address the long-tailed problem, our framework operates in three steps. Firstly, we partition the heavily long-tailed distribution into subsets of more balanced sub-distribution groups, including head, body, and tail classes with a predicate sampling module. Next, we establish a peer network consisting of multiple peers, where each peer receives a combination of sub-distributions. This division enables peers to focus on different aspects of the scene graph generation task. Then, a novel peer learning loss function is introduced to cultivate the learning process among peer networks. Lastly, we employ the voting strategies for making final predictions within the peer network, boosting the influence of the majority’s opinion while downplaying the minority’s perspective. To illustrate the applicability of the proposed framework in intelligent transportation systems (ITSs), we further conduct qualitative evaluations on traffic scene understanding tasks. The results demonstrate that peer learning markedly enhances the reliability of interpreting complex traffic scenarios. Experimental results on the Visual Genome and Open Images V6 datasets further verify the effectiveness of our proposed model. These results highlight that the peer learning framework is well-suited for addressing the challenges of unbiased scene graph generation, offering practical benefits for ITS applications such as traffic analysis and monitoring. The code is available at: PL.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".