Can we better anticipate an actor's future actions (e.g. mix eggs) by kn...
Visual Grounding (VG) aims at localizing target objects from an image ba...
Change detection is an essential and widely utilized task in remote sens...
Existing implicit neural representation (INR) methods do not fully explo...
Given the ubiquity of non-separable optimization problems in real worlds...
Humans have the innate capability to answer diverse questions, which is
...
Metaheuristic algorithms are widely-recognized solvers for challenging
o...
Metaheuristic algorithms have attracted wide attention from academia and...
Deep convolutional neural networks (DCNNs) based remote sensing (RS) ima...
In this paper, we present a pure-Python open-source library, called PyPo...
We study the limitations and fast-forwarding of quantum algorithms for
s...
Cell instance segmentation is a new and challenging task aiming at joint...
Ovarian cancer is one of the most harmful gynecological diseases. Detect...
In this paper, we propose a constrained heterogeneous facility location ...
While attention has been an increasingly popular component in deep neura...
Efficient quantum compiling tactics greatly enhance the capability of qu...
Metaheuristics are gradient-free and problem-independent search algorith...
Objective: The next generation prosthetic hand that moves and feels like...
Effectiveness and interpretability are two essential properties for
trus...
Face recognition has recently become ubiquitous in many scenes for
authe...
A key challenge for machine intelligence is to learn new visual concepts...
The learning process of deep learning methods usually updates the model'...
Recently, scene text detection has been a challenging task. Texts with
a...
Large-scale fine-grained image retrieval has two main problems. First, l...
Understanding the trustworthiness of a prediction yielded by a classifie...
Reconfigurable Intelligent Surface (RIS) is a revolutionizing approach t...
With the fast development of Deep Learning techniques, Named Entity
Reco...
Object detection on drone-captured scenarios is a recent popular task. A...
Few-shot semantic segmentation is a challenging task of predicting objec...
Gridless methods show great superiority in line spectral estimation. The...
The source number identification is an essential step in direction-of-ar...
In many clustering scenes, data samples' attribute values change over ti...
Remote sensing (RS) image scene classification task faces many challenge...
Objective: Deep learning-based neural decoders have emerged as the promi...
Deploying convolutional neural networks (CNNs) for embedded applications...
In the object detection task, CNN (Convolutional neural networks) models...
Remote sensing (RS) scene classification is a challenging task to predic...
This paper presents a deep learning framework based on Long Short-term M...
While attention has been an increasingly popular component in deep neura...
The last decades have seen great progress in saliency prediction, with t...
Few-shot object detection aims at detecting objects with few annotated
e...
Benefiting from deep learning research and large-scale datasets, salienc...
Consider a sequential active learning problem where, at each round, an a...
The computer vision community is witnessing an unprecedented rate of new...
One of the well-known challenges in computer vision tasks is the visual
...
Generative Adversarial Networks (GANs) have shown great success in many
...
The relationship between the intelligence and brain morphology is warmly...
Recently a new feature representation and data analysis methodology base...
Intra-class compactness and inter-class separability are crucial indicat...
Social relationships form the basis of social structure of humans. Devel...