Weakly Supervised Object Detection Using Complementary Learning and Instance Clustering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it