794 citations · 925 across the 17 of their papers we have counts for
18 papers
MetaVLA: Unified Meta Co-training For Efficient Embodied Adaption
Chen Li, Zhantao Yang, Han Zhang +4
Vision-Language-Action (VLA) models show promise in embodied reasoning, yet remain far from true generalists-they often require task-specific fine-tuning, incur high compute costs,…
EgoObjects: A Large-Scale Egocentric Dataset for Fine-Grained Object Understanding
Chenchen Zhu, Fanyi Xiao, Andres Alvarado +6
Object understanding in egocentric visual data is arguably a fundamental research topic in egocentric vision. However, existing object datasets are either non-egocentric or have li…
Exploring Open-Vocabulary Semantic Segmentation without Human Labels
Jun Chen, Deyao Zhu, Guocheng Qian +6
Semantic segmentation is a crucial task in computer vision that involves segmenting images into semantically meaningful regions at the pixel level. However, existing approaches oft…
Going Denser with Open-Vocabulary Part Segmentation
Peize Sun, Shoufa Chen, Chenchen Zhu +4
Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-gra…
3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding
Lorenzo Pellegrini, Chenchen Zhu, Fanyi Xiao +7
Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community. Recent research effor…
Enhanced Training of Query-Based Object Detection via Selective Query Recollection
Fangyi Chen, Han Zhang, Kai Hu +3
This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage. We review the tra…
Unitail: Detecting, Reading, and Matching in Retail Scene
Fangyi Chen, Han Zhang, Zaiwang Li +7
To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene. Pursuing this goal, we intro…
Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection
Chenchen Zhu, Fangyi Chen, Uzair Ahmed +2
Few-shot object detection is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data. Its performance is largely affected by the data s…
Solving Missing-Annotation Object Detection with Background Recalibration Loss
Han Zhang, Fangyi Chen, Zhiqiang Shen +3
This paper focuses on a novel and challenging detection scenario: A majority of true objects/instances is unlabeled in the datasets, so these missing-labeled areas will be regarded…
Soft Anchor-Point Object Detection
Chenchen Zhu, Fangyi Chen, Zhiqiang Shen +1
Recently, anchor-free detection methods have been through great progress. The major two families, anchor-point detection and key-point detection, are at opposite edges of the speed…
Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement
Zhiqiang Shen, Zhankui He, Wanyun Cui +4
Generic Image recognition is a fundamental and fairly important visual problem in computer vision. One of the major challenges of this task lies in the fact that single image usual…
MMDetection: Open MMLab Detection Toolbox and Benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang +22
We present MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules. The to…
Feature Selective Anchor-Free Module for Single-Shot Object Detection
Chenchen Zhu, Yihui He, Marios Savvides
We motivate and present feature selective anchor-free (FSAF) module, a simple and effective building block for single-shot object detectors. It can be plugged into single-shot dete…
Bounding Box Regression with Uncertainty for Accurate Object Detection
Yihui He, Chenchen Zhu, Jianren Wang +2
Large-scale object detection datasets (e.g., MS-COCO) try to define the ground truth bounding boxes as clear as possible. However, we observe that ambiguities are still introduced…
Seeing Small Faces from Robust Anchor's Perspective
Chenchen Zhu, Ran Tao, Khoa Luu +1
This paper introduces a novel anchor design to support anchor-based face detection for superior scale-invariant performance, especially on tiny faces. To achieve this, we explicitl…
Faster Than Real-time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses
Chandrasekhar Bhagavatula, Chenchen Zhu, Khoa Luu +1
Facial alignment involves finding a set of landmark points on an image with a known semantic meaning. However, this semantic meaning of landmark points is often lost in 2D approach…
Towards a Deep Learning Framework for Unconstrained Face Detection
Yutong Zheng, Chenchen Zhu, Khoa Luu +3
Robust face detection is one of the most important pre-processing steps to support facial expression analysis, facial landmarking, face recognition, pose estimation, building of 3D…
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Chenchen Zhu, Yutong Zheng, Khoa Luu +1
Robust face detection in the wild is one of the ultimate components to support various facial related problems, i.e. unconstrained face recognition, facial periocular recognition,…