Weakly Supervised Object Detection Using Complementary Learning and Instance Clustering
Bibliographic record
Abstract
Supervised object detection schemes use fully annotated training data, which is fairly expensive to constitute. Whereas, weakly supervised object detection (WSOD) uses only image-level annotations for training which are much simpler to acquire. WSOD is a challenging task since it aims to learn object localization and detection with image-level labels. In line with this assertion, in this paper, we present an end-to-end framework for WSOD based on discriminative feature learning. We use the objectness technique to get initial proposals from the images. Afterwards, two complementary networks are trained in parallel to obtain discriminative image features, which are channel-wise concatenated with the features of the third network. We name this classification network designed for discriminative feature learning as fused complementary network. This network learns the proposals enclosing whole object instances by complementary features which ultimately learns to predict the high probabilities for whole objects than proposals containing only object parts. Clustering is then hierarchically performed on the region proposals. Our clustering method, named instance clustering, first performs inter-class clustering followed by iterative intra-class clustering using intersection-over-union metric to obtain spatially adjacent cluster members corresponding to each object instance. In each intra-class clustering iteration, the high scoring proposal is set as centroid from each intra-class cluster. Experiments are conducted on PASCAL VOC2007 and PASCAL VOC2012 datasets. Both qualitative and quantitative results have shown improved WSOD performance on these benchmarks.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".