Pith. sign in

REVIEW 1 cited by

Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2210.00174 v1 pith:XAMOY25W submitted 2022-10-01 cs.CV cs.LG

Li Gu , Zhixiang Chi , Huan Liu , Yuanhao Yu , Yang Wang This is my paper · ORCID
classification cs.CVcs.LG
keywords videochallengefew-shotimprovedobjectorbitperformanceprotonet
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this work, we present the winning solution for ORBIT Few-Shot Video Object Recognition Challenge 2022. Built upon the ProtoNet baseline, the performance of our method is improved with three effective techniques. These techniques include the embedding adaptation, the uniform video clip sampler and the invalid frame detection. In addition, we re-factor and re-implement the official codebase to encourage modularity, compatibility and improved performance. Our implementation accelerates the data loading in both training and testing.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MetaWriter: Personalized Handwritten Text Recognition Using Meta-Learned Prompt Tuning

    cs.CV 2025-05 conditional novelty 5.0 of 10

    MetaWriter uses meta-learned prompt tuning with an image-reconstruction auxiliary task to adapt a handwritten text recognizer to new writers from unlabeled examples, reporting lower error rates on IAM and RIMES with l...

Pith tools