Scene Classification in Indoor Environments for Robots using Context\n Based Word Embeddings
Bibliographic record
Abstract
Scene Classification has been addressed with numerous techniques in computer\nvision literature. However, with the increasing number of scene classes in\ndatasets in the field, it has become difficult to achieve high accuracy in the\ncontext of robotics. In this paper, we implement an approach which combines\ntraditional deep learning techniques with natural language processing methods\nto generate a word embedding based Scene Classification algorithm. We use the\nkey idea that context (objects in the scene) of an image should be\nrepresentative of the scene label meaning a group of objects could assist to\npredict the scene class. Objects present in the scene are represented by\nvectors and the images are re-classified based on the objects present in the\nscene to refine the initial classification by a Convolutional Neural Network\n(CNN). In our approach we address indoor Scene Classification task using a\nmodel trained with a reduced pre-processed version of the Places365 dataset and\nan empirical analysis is done on a real-world dataset that we built by\ncapturing image sequences using a GoPro camera. We also report results obtained\non a subset of the Places365 dataset using our approach and additionally show a\ndeployment of our approach on a robot operating in a real-world environment.\n
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".