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Found 18 papers, 12 papers with code
Mixed-Query Transformer: A Unified Image Segmentation Architecture
Existing unified image segmentation models either employ a unified architecture across multiple tasks but use separate weights tailored to each dataset, or apply a single set of weights to multiple datasets but are limited to a single task.
Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts
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, resulting in a single model which we named Musketeer.
PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
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 predicted polygons can be later converted into segmentation masks.
Ranked #1 on
Referring Expression Segmentation
on ReferIt
(using extra training data)
Semi-supervised Vision Transformers at Scale
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.
Masked Vision and Language Modeling for Multi-modal Representation Learning
Instead of developing masked language modeling (MLM) and masked image modeling (MIM) independently, we propose to build joint masked vision and language modeling, where the masked signal of one modality is reconstructed with the help from another modality.
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
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.
X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks
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.
Omni-DETR: Omni-Supervised Object Detection with Transformers
This is enabled by a unified architecture, Omni-DETR, based on the recent progress on student-teacher framework and end-to-end transformer based object detection.
Ranked #14 on
Semi-Supervised Object Detection
on COCO 2% labeled data
Contrastive Neighborhood Alignment
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 representations by the source (teacher) model are also mapped to neighbors by the target (student) model.
Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning
We present a plug-in replacement for batch normalization (BN) called exponential moving average normalization (EMAN), which improves the performance of existing student-teacher based self- and semi-supervised learning techniques.
Self-Supervised Learning
Semi-Supervised Image Classification
Rethinking Differentiable Search for Mixed-Precision Neural Networks
Low-precision networks, with weights and activations quantized to low bit-width, are widely used to accelerate inference on edge devices.
Cascade R-CNN: High Quality Object Detection and Instance Segmentation
In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives.
Ranked #4 on
Instance Segmentation
on BDD100K val
Towards Universal Object Detection by Domain Attention
Experiments, on a newly established universal object detection benchmark of 11 diverse datasets, show that the proposed detector outperforms a bank of individual detectors, a multi-domain detector, and a baseline universal detector, with a 1. 3x parameter increase over a single-domain baseline detector.
Cascade R-CNN: Delving into High Quality Object Detection
In object detection, an intersection over union (IoU) threshold is required to define positives and negatives.
Ranked #4 on
2D Object Detection
on SARDet-100K
Deep Learning with Low Precision by Half-wave Gaussian Quantization
The problem of quantizing the activations of a deep neural network is considered.
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection.
Ranked #24 on
Pedestrian Detection
on Caltech
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset.
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
CompACT cascades are shown to seek an optimal trade-off between accuracy and complexity by pushing features of higher complexity to the later cascade stages, where only a few difficult candidate patches remain to be classified.
Ranked #26 on
Pedestrian Detection
on Caltech
