TIDE: Test Time Few Shot Object Detection

• URL: http://arxiv.org/abs/2311.18358v1
• Date: Thu, 30 Nov 2023 09:00:44 GMT
• Title: TIDE: Test Time Few Shot Object Detection
• Authors: Weikai Li, Hongfeng Wei, Yanlai Wu, Jie Yang, Yudi Ruan, Yuan Li and Ying Tang
• Abstract summary: Few-shot object detection (FSOD) aims to extract semantic knowledge from limited object instances of novel categories within a target domain. Recent advances in FSOD focus on fine-tuning the base model based on a few objects via meta-learning or data augmentation. We formalize a novel FSOD task, referred to as Test TIme Few Shot DEtection (TIDE), where the model is un-tuned in the configuration procedure.
• Score: 11.036762620105383
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Few-shot object detection (FSOD) aims to extract semantic knowledge from limited object instances of novel categories within a target domain. Recent advances in FSOD focus on fine-tuning the base model based on a few objects via meta-learning or data augmentation. Despite their success, the majority of them are grounded with parametric readjustment to generalize on novel objects, which face considerable challenges in Industry 5.0, such as (i) a certain amount of fine-tuning time is required, and (ii) the parameters of the constructed model being unavailable due to the privilege protection, making the fine-tuning fail. Such constraints naturally limit its application in scenarios with real-time configuration requirements or within black-box settings. To tackle the challenges mentioned above, we formalize a novel FSOD task, referred to as Test TIme Few Shot DEtection (TIDE), where the model is un-tuned in the configuration procedure. To that end, we introduce an asymmetric architecture for learning a support-instance-guided dynamic category classifier. Further, a cross-attention module and a multi-scale resizer are provided to enhance the model performance. Experimental results on multiple few-shot object detection platforms reveal that the proposed TIDE significantly outperforms existing contemporary methods. The implementation codes are available at https://github.com/deku-0621/TIDE

Related papers

• Is Multiple Object Tracking a Matter of Specialization? [33.59920084936913]
  Training end-to-end transformer-based trackers in heterogeneous scenarios poses significant challenges. We introduce. The Scenario-specific Tracking Architecture (PASTA), a novel framework that combines. Efficient Fine-Tuning (PEFT) and Modular Deep Learning.
  arXiv  Detail & Related papers  (2024-11-01T13:03:58Z)
• Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector [72.05791402494727]
  This paper studies the challenging cross-domain few-shot object detection (CD-FSOD) It aims to develop an accurate object detector for novel domains with minimal labeled examples.
  arXiv  Detail & Related papers  (2024-02-05T15:25:32Z)
• FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects [55.77542145604758]
  FoundationPose is a unified foundation model for 6D object pose estimation and tracking. Our approach can be instantly applied at test-time to a novel object without fine-tuning.
  arXiv  Detail & Related papers  (2023-12-13T18:28:09Z)
• Open World Object Detection in the Era of Foundation Models [53.683963161370585]
  We introduce a new benchmark that includes five real-world application-driven datasets. We introduce a novel method, Foundation Object detection Model for the Open world, or FOMO, which identifies unknown objects based on their shared attributes with the base known objects.
  arXiv  Detail & Related papers  (2023-12-10T03:56:06Z)
• MFOS: Model-Free & One-Shot Object Pose Estimation [10.009454818723025]
  We introduce a novel approach that can estimate in a single forward pass the pose of objects never seen during training, given minimum input. We conduct extensive experiments and report state-of-the-art one-shot performance on the challenging LINEMOD benchmark.
  arXiv  Detail & Related papers  (2023-10-03T09:12:07Z)
• Scaling Novel Object Detection with Weakly Supervised Detection Transformers [21.219817483091166]
  We propose the Weakly Supervised Detection Transformer, which enables efficient knowledge transfer from a large-scale pretraining dataset to WSOD finetuning. Our experiments show that our approach outperforms previous state-of-the-art models on large-scale novel object detection datasets.
  arXiv  Detail & Related papers  (2022-07-11T21:45:54Z)
• Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning [60.64535309016623]
  We propose the Incremental-DETR that does incremental few-shot object detection via fine-tuning and self-supervised learning on the DETR object detector. To alleviate severe over-fitting with few novel class data, we first fine-tune the class-specific components of DETR with self-supervision. We further introduce a incremental few-shot fine-tuning strategy with knowledge distillation on the class-specific components of DETR to encourage the network in detecting novel classes without catastrophic forgetting.
  arXiv  Detail & Related papers  (2022-05-09T05:08:08Z)
• Plug-and-Play Few-shot Object Detection with Meta Strategy and Explicit Localization Inference [78.41932738265345]
  This paper proposes a plug detector that can accurately detect the objects of novel categories without fine-tuning process. We introduce two explicit inferences into the localization process to reduce its dependence on annotated data. It shows a significant lead in both efficiency, precision, and recall under varied evaluation protocols.
  arXiv  Detail & Related papers  (2021-10-26T03:09:57Z)
• Incremental Few-Shot Object Detection [96.02543873402813]
  OpeN-ended Centre nEt is a detector for incrementally learning to detect class objects with few examples. ONCE fully respects the incremental learning paradigm, with novel class registration requiring only a single forward pass of few-shot training samples.
  arXiv  Detail & Related papers  (2020-03-10T12:56:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.