Papers › Synthesizing the Unseen for Zero-shot Object Detection
Synthesizing the Unseen for Zero-shot Object Detection
Nasir Hayat, Munawar Hayat, Shafin Rahman, Salman Khan, Syed Waqas Zamir, Fahad Shahbaz Khan
The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during inference. However, since the unseen objects are never visualized during training, the detection model is skewed towards seen content, thereby labeling unseen as background or a seen class. In this work, we propose to synthesize visual features for unseen classes, so that the model learns both seen and unseen objects in the visual domain. Consequently, the major challenge becomes, how to accurately synthesize unseen objects merely using their class semantics? Towards this ambitious goal, we propose a novel generative model that uses class-semantics to not only generate the features but also to discriminatively separate them. Further, using a unified model, we ensure the synthesized features have high diversity that represents the intra-class differences and variable localization precision in the detected bounding boxes. We test our approach on three object detection benchmarks, PASCAL VOC, MSCOCO, and ILSVRC detection, under both conventional and generalized settings, showing impressive gains over the state-of-the-art methods. Our codes are available at https://github.com/nasir6/zero_shot_detection.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero-Shot Object Detection | ImageNet Detection | SUZOD | mAP | 24.3 | #1 of 1 | Archive leaderboard | report |
| Zero-Shot Object Detection | MS-COCO | SUZOD | Recall | 61.40 | #6 of 9 | Archive leaderboard | report |
| Zero-Shot Object Detection | MS-COCO | SUZOD | mAP | 17.30 | #6 of 9 | Archive leaderboard | report |
| Zero-Shot Object Detection | PASCAL VOC'07 | SUZOD | mAP | 64.9 | #3 of 7 | 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.
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