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pro vyhledávání: '"Bhatnagar, Shubhang"'
Vision-language models (VLMs) like CLIP have been adapted for Multi-Label Recognition (MLR) with partial annotations by leveraging prompt-learning, where positive and negative prompts are learned for each class to associate their embeddings with clas
Externí odkaz:
http://arxiv.org/abs/2409.08381
Autor:
Bhatnagar, Shubhang, Ahuja, Narendra
Deep metric learning (DML) involves training a network to learn a semantically meaningful representation space. Many current approaches mine n-tuples of examples and model interactions within each tuplets. We present a novel, compositional DML model,
Externí odkaz:
http://arxiv.org/abs/2405.18560
Multi-label Recognition (MLR) involves the identification of multiple objects within an image. To address the additional complexity of this problem, recent works have leveraged information from vision-language models (VLMs) trained on large text-imag
Externí odkaz:
http://arxiv.org/abs/2404.16193
Autor:
Bhatnagar, Shubhang, Ahuja, Narendra
Unsupervised deep metric learning (UDML) focuses on learning a semantic representation space using only unlabeled data. This challenging problem requires accurately estimating the similarity between data points, which is used to supervise a deep netw
Externí odkaz:
http://arxiv.org/abs/2403.14977
Publikováno v:
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA, 2023, pp. 1307-1312
Gestures form an important medium of communication between humans and machines. An overwhelming majority of existing gesture recognition methods are tailored to a scenario where humans and machines are located very close to each other. This short-dis
Externí odkaz:
http://arxiv.org/abs/2308.04643
Training CNNs from scratch on new domains typically demands large numbers of labeled images and computations, which is not suitable for low-power hardware. One way to reduce these requirements is to modularize the CNN architecture and freeze the weig
Externí odkaz:
http://arxiv.org/abs/2110.10969
Compressed sensing (CS) involves sampling signals at rates less than their Nyquist rates and attempting to reconstruct them after sample acquisition. Most such algorithms have parameters, for example the regularization parameter in LASSO, which need
Externí odkaz:
http://arxiv.org/abs/2102.10165
The goal of pool-based active learning is to judiciously select a fixed-sized subset of unlabeled samples from a pool to query an oracle for their labels, in order to maximize the accuracy of a supervised learner. However, the unsaid requirement that
Externí odkaz:
http://arxiv.org/abs/2010.15947
Autor:
Bhatnagar, Ishan, Bhatnagar, Shubhang
We propose a novel algorithm for using Hopfield networks to denoise QR codes. Hopfield networks have mostly been used as a noise tolerant memory or to solve difficult combinatorial problems. One of the major drawbacks in their use in noise tolerant a
Externí odkaz:
http://arxiv.org/abs/1812.01065
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