Background Learnable Cascade for Zero-Shot Object Detection

Zheng, Ye; Huang, Ruoran; Han, Caixia; Huang, Xi; Li, Cui

doi:10.1007/978-3-030-69535-4_7

Cited by 40 publications

(28 citation statements)

References 43 publications

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“…For the 47/17 split, our method outperforms all the compared methods by a large margin in terms of both Recall@100 and mAP measurements. Compared with the second-best method BLC [42], our method improves the Recall@100 by 9.6 % and mAP by 26.4 % at IoU=0.5. For the 65/15 split, we can observe that our method also achieves a significant performance gain, which improves the Recall@100 and mAP of method SU [16] from 54.0 % and 19.0 % to 62.3 % and 19.8 % at IoU=0.5.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 91%

“…Zero-shot object detection. ZSD receives great research interest in recent years [4,7,16,22,31,32,[41][42][43]. Some researches focus on embedding function-based methods [4,7,22,31,32,42].…”

Section: Related Workmentioning

confidence: 99%

“…ZSD receives great research interest in recent years [4,7,16,22,31,32,[41][42][43]. Some researches focus on embedding function-based methods [4,7,22,31,32,42]. Unfortunately, these methods would suffer from the overfitting problem like in ZSL, where the unseen objects are significantly biased towards the seen classes or background [16].…”

Section: Related Workmentioning

confidence: 99%

“…Our method outperforms all the comparison methods over two splits in terms of all the metrics. Compared with the second-best method BLC [42], our method improves the "HM" performance in Recall@100 and mAP We report clas-wise AP of our method in Table 5 for 65/15 split. Our method achieves the best performance on 9 out of 15 classes and comparable performance on other classes.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

See 3 more Smart Citations

Robust Region Feature Synthesizer for Zero-Shot Object Detection

Huang¹,

Han²,

Cheng³

et al. 2022

Preprint

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Zero-shot object detection aims at incorporating class semantic vectors to realize the detection of (both seen and) unseen classes given an unconstrained test image. In this study, we reveal the core challenges in this research area: how to synthesize robust region features (for unseen objects) that are as intra-class diverse and inter-class separable as the real samples, so that strong unseen object detectors can be trained upon them. To address these challenges, we build a novel zero-shot object detection framework that contains an Intra-class Semantic Diverging component and an Inter-class Structure Preserving component. The former is used to realize the one-to-more mapping to obtain diverse visual features from each class semantic vector, preventing miss-classifying the real unseen objects as image backgrounds. While the latter is used to avoid the synthesized features too scattered to mix up the inter-class and foreground-background relationship. To demonstrate the effectiveness of the proposed approach, comprehensive experiments on PASCAL VOC, COCO, and DIOR datasets are conducted. Notably, our approach achieves the new state-of-the-art performance on PASCAL VOC and COCO and it is the first study to carry out zero-shot object detection in remote sensing imagery.

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Section: Comparison With the State-of-the-artmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

See 2 more Smart Citations

Robust Region Feature Synthesizer for Zero-Shot Object Detection

Huang¹,

Han²,

Cheng³

et al. 2022

Preprint

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show abstract

“…In zero-shot classification [28,29] and recognition [30,31,32,33], word embeddings commonly replace learnable class prototypes to transfer from training classes to unseen classes using inherit semantic relationships extracted from text corpora. Commonly used word embeddings are GloVe vectors [29,30] and word2vec embeddings [31,34,33,35], however embeddings learnt from image-text pairs using the CLIP [36] achieve the best zero-shot performance so far [32].…”

Section: Knowledge-based Embeddingsmentioning

confidence: 99%

Contrastive Object Detection Using Knowledge Graph Embeddings

Lang¹,

Braun²,

Valada³

2021

Preprint

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Object recognition for the most part has been approached as a one-hot problem that treats classes to be discrete and unrelated. Each image region has to be assigned to one member of a set of objects, including a background class, disregarding any similarities in the object types. In this work, we compare the error statistics of the class embeddings learned from a one-hot approach with semantically structured embeddings from natural language processing or knowledge graphs that are widely applied in open world object detection. Extensive experimental results on multiple knowledge-embeddings as well as distance metrics indicate that knowledge-based class representations result in more semantically grounded misclassifications while performing on par compared to one-hot methods on the challenging COCO and Cityscapes object detection benchmarks. We generalize our findings to multiple object detection architectures by proposing a knowledge-embedded design knowledge graph embedded (KGE) for keypoint-based and transformer-based object detection architectures.

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Transformer-Based Zero-Shot Detection via Contrastive Learning

Liu,

Chen,

et al. 2022

IFIP Advances in Information and Communication Technology

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Background Learnable Cascade for Zero-Shot Object Detection

Cited by 40 publications

References 43 publications

Robust Region Feature Synthesizer for Zero-Shot Object Detection

Robust Region Feature Synthesizer for Zero-Shot Object Detection

Contrastive Object Detection Using Knowledge Graph Embeddings

Transformer-Based Zero-Shot Detection via Contrastive Learning

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