Learning Efficient and Adaptive Cross-Channel Dependencies for Weakly-Supervised Object Detection
Bibliographic record
Abstract
Recent progress in weakly-supervised object detection (WSOD) is featured by a combination of multiple instance detection networks (MIDN) and ordinal online refinement. However, since most WSOD methods only use image-level annotations, the serial stacking of convolutional blocks in MIDN cannot effectively model multi-channel information, often emphasizing only the most prominent parts of the target while ignoring the entire objects, thus affecting detection performance. In this paper, we investigate how to effectively use multi-channel data to improve the model's ability to detect long-range dependencies, introducing CC-DETR (Cross-Channel DETR), a new weakly-supervised object detection framework. Specifically, we propose Cross-Channel Adaptive Convolution (CCAC), a module that captures different spatial features at multiple scales, increases the receptive field, and adaptively weights each important feature to guide the model to focus on long-term dependencies. Moreover, we designed a new attention mechanism called Dual-Stream Self-Attention (DSSA). This mechanism uses convolutions with adaptive sizes to capture multi-scale information, preserving long-range dependencies while supporting local feature responses, enhancing the model's ability to capture long-range dependencies. Extensive experiments demonstrate that our proposed method outperforms the current end-to-end state of the art (+2.3% mAP in VOC, +2.3% AP$_{50}$in COCO). Moreover, our method can be easily integrated into various DETR and ViT models with minimal modifications. The code will be available athttps://github.com/cpy0029/CC-DETR.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".