398 citations · 865 across the 17 of their papers we have counts for
17 papers
Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts
Zhaoyang Zhang, Yantao Shen, Kunyu Shi +7
We present a vision-language model whose parameters are jointly trained on all tasks and fully shared among multiple heterogeneous tasks which may interfere with each other, result…
PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
Jiang Liu, Hui Ding, Zhaowei Cai +4
In this work, instead of directly predicting the pixel-level segmentation masks, the problem of referring image segmentation is formulated as sequential polygon generation, and the…
Semi-supervised Vision Transformers at Scale
Zhaowei Cai, Avinash Ravichandran, Paolo Favaro +5
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this…
Masked Vision and Language Modeling for Multi-modal Representation Learning
Gukyeong Kwon, Zhaowei Cai, Avinash Ravichandran +3
In this paper, we study how to use masked signal modeling in vision and language (V+L) representation learning. Instead of developing masked language modeling (MLM) and masked imag…
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Kibok Lee, Hao Yang, Satyaki Chakraborty +4
Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-training and few-shot learning datasets are from a similar domain. However, few-shot algor…
X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks
Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran +4
In this paper, we study the challenging instance-wise vision-language tasks, where the free-form language is required to align with the objects instead of the whole image. To addre…
Omni-DETR: Omni-Supervised Object Detection with Transformers
Pei Wang, Zhaowei Cai, Hao Yang +4
We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for ob…
Contrastive Neighborhood Alignment
Pengkai Zhu, Zhaowei Cai, Yuanjun Xiong +4
We present Contrastive Neighborhood Alignment (CNA), a manifold learning approach to maintain the topology of learned features whereby data points that are mapped to nearby represe…
Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning
Zhaowei Cai, Avinash Ravichandran, Subhransu Maji +3
We present a plug-in replacement for batch normalization (BN) called exponential moving average normalization (EMAN), which improves the performance of existing student-teacher bas…
Rethinking Differentiable Search for Mixed-Precision Neural Networks
Zhaowei Cai, Nuno Vasconcelos
Low-precision networks, with weights and activations quantized to low bit-width, are widely used to accelerate inference on edge devices. However, current solutions are uniform, us…
Cascade R-CNN: High Quality Object Detection and Instance Segmentation
Zhaowei Cai, Nuno Vasconcelos
In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its \textit{qualit…
Towards Universal Object Detection by Domain Attention
Xudong Wang, Zhaowei Cai, Dashan Gao +1
Despite increasing efforts on universal representations for visual recognition, few have addressed object detection. In this paper, we develop an effective and efficient universal…
Cascade R-CNN: Delving into High Quality Object Detection
Zhaowei Cai, Nuno Vasconcelos
In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually…
Deep Learning with Low Precision by Half-wave Gaussian Quantization
Zhaowei Cai, Xiaodong He, Jian Sun +1
The problem of quantizing the activations of a deep neural network is considered. An examination of the popular binary quantization approach shows that this consists of approximati…
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Zhaowei Cai, Quanfu Fan, Rogerio S. Feris +1
A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MS-CNN consists of a proposal sub-network and a detectio…
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
Longyin Wen, Dawei Du, Zhaowei Cai +6
In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usu…
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
Zhaowei Cai, Mohammad Saberian, Nuno Vasconcelos
The design of complexity-aware cascaded detectors, combining features of very different complexities, is considered. A new cascade design procedure is introduced, by formulating ca…