Papers › Attention-based Joint Detection of Object and Semantic Part
Attention-based Joint Detection of Object and Semantic Part
Keval Morabia, Jatin Arora, Tara Vijaykumar
In this paper, we address the problem of joint detection of objects like dog and its semantic parts like face, leg, etc. Our model is created on top of two Faster-RCNN models that share their features to perform a novel Attention-based feature fusion of related Object and Part features to get enhanced representations of both. These representations are used for final classification and bounding box regression separately for both models. Our experiments on the PASCAL-Part 2010 dataset show that joint detection can simultaneously improve both object detection and part detection in terms of mean Average Precision (mAP) at IoU=0.5.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection | PASCAL Part 2010 - Animals | Attention-based Joint Detection of Object and Semantic Part | mAP@0.5 | 87.5 | #1 of 1 | Archive leaderboard | report |
| Semantic Part Detection | PASCAL Part 2010 - Animals | Attention-based Joint Detection of Object and Semantic Part | mAP@0.5 | 52.0 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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