groundai.com

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GroundAI - Every ArXiv paper needs to be discussed

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Every arXiv paper needs to be discussed. GroundAI is a place for machine learning researchers to get feedback and gain insights to improve their work.

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1	Every arxiv paper needs to be discussed

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1	Deformable siamese attention networks for visual object tracking
2	Finegym: a hierarchical video dataset for fine-grained action under…
3	Dialgraph: spa**** graph learning networks for visual dialog
4	End-to-end variational networks for accelerated mrı reconstruction
5	Multi-resolution a*
6	Rapid damage a***essment using social media ımages by combining human …
7	Exploring cell counting with neural arithmetic logic units
8	An automatic covıd-19 ct segmentation based on u-net with attention …
9	Co-eye: a multi-resolution symbolic representation to timeseries di…
10	Weight poisoning attacks on pre-trained models
11	A transfer learning approach to heatmap regression for action unit …
12	A text-based deep reinforcement learning framework for ınteractive …
13	An attention-based system for damage a***essment using satellite ımage…
14	Distilling localization for self-supervised representation learning
15	Occupant plugload management for demand response in commercial buil…
16	Edgeworth expansions for network moments
17	Pretrained transformers ımprove out-of-distribution robustness
18	Topology of deep neural networks
19	Thinking while moving: deep reinforcement learning with concurrent …
20	Multi-stage jamming attacks detection using deep learning combined wi…

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No	Text	Type
1	Home	text
2	Library	text
3	Discussions	text
4	About	text
5	Advanced Search	text
6	☰	text
7	Home	text
8	Discussions	text
9	About	text
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15	-	empty
16	Deformable Siamese Attention Networks for Visual Object Tracking Siamese-based trackers have achieved excellent performance on visual object tracking. However, the target template is not updated online, and the fea…	text
17	-	empty
18	FineGym: A Hierarchical Video Dataset for Fine-grained Action Under… On public benchmarks, current action recognition techniques have achieved great success. However, when used in real-world applications, e.g. sport an…	text
19	In this paper, we formulate the visual dialog tasks as a graph structure learning tasks where the edges represent the semantic dependencies among the multimodal embedding nodes learned from the given image, caption and question, and dialog history. …	text
20	DialGraph: Spa**** Graph Learning Networks for Visual Dialog Visual dialog is a task of answering a sequence of questions grounded in an image utilizing a dialog history. Previous studies have implicitly explor…	text
21	-	empty
22	End-to-End Variational Networks for Accelerated MRI Reconstruction The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of t…	text
23	-	empty
24	Multi-Resolution A* Heuristic search-based planning techniques are commonly used for motion planning on discretized spaces. The performance of these algorithms is heavil…	text
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26	Rapid Damage a*essment Using Social Media Images by Combining Human … Rapid damage a*essment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to …	text
27	-	empty
28	Exploring Cell counting with Neural Arithmetic Logic Units The big problem for neural network models which are trained to count instances is that whenever test range goes high training range generalization er…	text
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30	An automatic COVID-19 CT segmentation based on U-Net with attention … The coronavirus disease (COVID-19) pandemic has led a devastating effect on the global public health. Computed Tomography (CT) is an effective tool i…	text
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32	Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Di… Time series cla***ification (TSC) is a challenging task that attracted many researchers in the last few years. One main challenge in TSC is the divers…	text
33	-	empty
34	Weight Poisoning Attacks on Pre-trained Models Recently, NLP has seen a surge in the usage of large pre-trained models. Users download weights of models pre-trained on large datasets, then fine-tu…	text
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36	A Transfer Learning approach to Heatmap Regression for Action Unit … Action Units (AUs) are geometrically-based atomic facial muscle movements known to produce appearance changes at specific facial locations. Motivated…	text
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38	A Text-based Deep Reinforcement Learning Framework for Interactive … Due to its nature of learning from dynamic interactions and planning for long-run performance, reinforcement learning (RL) recently has received much…	text
39	-	empty
40	An Attention-Based System for Damage a***essment Using Satellite Image… When disaster strikes, accurate situational information and a fast, effective response are critical to save lives. Widely available, high resolution …	text
41	-	empty
42	Distilling Localization for Self-Supervised Representation Learning For high-level visual recognition, self-supervised learning defines and makes use of proxy tasks such as colorization and visual tracking to learn a …	text
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44	Occupant Plugload Management for Demand Response in Commercial Buil… Commercial buildings account for ~36% of US electricity consumption, of which nearly two-thirds is met by fossil fuels [1]. Reducing this impact on t…	text
45	-	empty
46	Edgeworth expansions for network moments Network method of moments arXiv:1202.5101 is an important tool for nonparametric network inferences. However, there has been little investigation on …	text
47	-	empty
48	Pretrained Transformers Improve Out-of-Distribution Robustness Although pretrained Transformers such as BERT achieve high accuracy on in-distribution examples, do they generalize to new distributions? We systemat…	text
49	-	empty
50	Topology of deep neural networks We study how the topology of a data set $M = M_a \cup M_b \subseteq \mathbb{R}^d$, representing two cla*es $a$ and $b$ in a binary cla*ification pr…	text