5 papers
Boosting Text-Driven Video Segmentation via Geometry-Aware Distillation
Tianyu Zhu, Yingping Liang, Hesong Li +1
Text-driven Referring Video Object Segmentation (RVOS) aims to locate and segment target objects in videos given natural language. However, existing models are typically trained on…
Knowledge Combination to Learn Rotated Detection Without Rotated Annotation
Tianyu Zhu, Bryce Ferenczi, Pulak Purkait +3
Rotated bounding boxes drastically reduce output ambiguity of elongated objects, making it superior to axis-aligned bounding boxes. Despite the effectiveness, rotated detectors are…
Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning
Rongkai Ma, Pengfei Fang, Gil Avraham +4
Learning and generalizing to novel concepts with few samples (Few-Shot Learning) is still an essential challenge to real-world applications. A principle way of achieving few-shot l…
Learning Online for Unified Segmentation and Tracking Models
Tianyu Zhu, Rongkai Ma, Mehrtash Harandi +1
Tracking requires building a discriminative model for the target in the inference stage. An effective way to achieve this is online learning, which can comfortably outperform model…
Learn to Predict Sets Using Feed-Forward Neural Networks
Hamid Rezatofighi, Tianyu Zhu, Roman Kaskman +6
This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a se…