In addition, Subtype-Former also achieved outstanding results in pan-cancer subtyping, which can help analyze the commonalities and differences across various cancer types at the molecular level.
In recent years, video instance segmentation (VIS) has been largely advanced by offline models, while online models gradually attracted less attention possibly due to their inferior performance.
We present a unified method, termed Unicorn, that can simultaneously solve four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters.
Ranked #1 on Multiple Object Tracking on BDD100K
X-ray ptychography allows for large fields to be imaged at high resolution at the cost of additional computational expense due to the large volume of data.
This paper studies the optimal state estimation for a dynamic system, whose transfer function can be nonlinear and the input noise can be of arbitrary distribution.
Fine-Grained Visual Classification(FGVC) is the task that requires recognizing the objects belonging to multiple subordinate categories of a super-category.
Ranked #1 on Image Classification on iNaturalist 2018 (using extra training data)
As an interesting by-product, we propose an enhancement to the BLE standard by introducing a bit interleaver to its physical layer; the resultant improvement of the receiver sensitivity can make it a better fit for some Internet of Things (IoT) communications.
Referring video object segmentation (R-VOS) is an emerging cross-modal task that aims to segment the target object referred by a language expression in all video frames.
Ranked #1 on Referring Expression Segmentation on Refer-YouTube-VOS (2021 public validation) (using extra training data)
Nevertheless, we observe that a stand-alone instance query suffices for capturing a time sequence of instances in a video, but attention mechanisms shall be done with each frame independently.
Ranked #4 on Video Instance Segmentation on YouTube-VIS validation (using extra training data)
A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association.
Vision-and-Language Navigation (VLN) is a task that an agent is required to follow a language instruction to navigate to the goal position, which relies on the ongoing interactions with the environment during moving.
ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.
Ranked #1 on Multi-Object Tracking on MOT20 (using extra training data)
A more realistic object detection paradigm, Open-World Object Detection, has arisen increasing research interests in the community recently.
This paper proposes a novel scheme for mitigating strong interferences, which is applicable to various wireless scenarios, including full-duplex wireless communications and uncoordinated heterogenous networks.
To remove such a requirement, an off-policy reinforcement learning algorithm is proposed using only the measured output data along the trajectories of the system and the reference output.
We propose a network approach to determine the optimal low resources setting oriented pool testing strategies that identifies infected individuals in a small number of tests and few rounds of testing, at low prevalence of the virus.
Physics and Society Social and Information Networks
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems.
Ranked #4 on Multi-Object Tracking on DanceTrack
We identify that classification cost in matching cost is the main ingredient: (1) previous detectors only consider location cost, (2) by additionally introducing classification cost, previous detectors immediately produce one-to-one prediction during inference.
no code implementations • 8 Dec 2020 • Kayla X. Nguyen, Yi Jiang, Michael C. Cao, Prafull Purohit, Ajay K. Yadav, Pablo García-Fernández, Mark W. Tate, Celesta S. Chang, Pablo Aguado-Puente, Jorge Íñiguez, Fernando Gomez-Ortiz, Sol M. Gruner, Javier Junquera, Lane W. Martin, Ramamoorthy Ramesh, D. A. Muller
We find that the presence of an electric toroidal moment in a ferro-rotational phase transfers a measurable torque and orbital angular momentum to the electron beam.
Materials Science Applied Physics
In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.
Ranked #75 on Object Detection on COCO minival
In the last two decades, scholars have designed various types of bibliographic related indicators to identify breakthrough-class academic achievements.
no code implementations • 21 Aug 2020 • Zhang Li, Jiehua Zhang, Tao Tan, Xichao Teng, Xiaoliang Sun, Yang Li, Lihong Liu, Yang Xiao, Byungjae Lee, Yilong Li, Qianni Zhang, Shujiao Sun, Yushan Zheng, Junyu Yan, Ni Li, Yiyu Hong, Junsu Ko, Hyun Jung, Yanling Liu, Yu-cheng Chen, Ching-Wei Wang, Vladimir Yurovskiy, Pavel Maevskikh, Vahid Khanagha, Yi Jiang, Xiangjun Feng, Zhihong Liu, Daiqiang Li, Peter J. Schüffler, Qifeng Yu, Hui Chen, Yuling Tang, Geert Litjens
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
Popularized by compressed sensing, sparsity-exploiting algorithms have been applied to experimental ET data and show promise for improving reconstruction quality or reducing the total beam dose applied to a specimen.
A Simple and Versatile Framework for Object Detection and Instance Recognition
Highly-directional image artifacts such as ion mill curtaining, mechanical scratches, or image striping from beam instability degrade the interpretability of micrographs.