The performance of image retrieval depends critically on the semantic representation and the distance function used to estimate the similarity of two images. A good representation should integrate multiple visual and textual (e.g., tag) features and offer a step closer to the true semantics of int...
Learning A Sparse Transformer Network for Effective Image Deraining Xiang Chen1 Hao Li1 Mingqiang Li2 Jinshan Pan1 1School of Computer Science and Engineering, Nanjing University of Science and Technology 2Information Science Academy, China Electronics Technology Group Corporation Overview In this ...
An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in the b
meta_learning_without_memorization meta_pseudo_labels meta_reward_learning metapose mface mico micronet_challenge microscope_image_quality milking_cowmask minigrid_basics misinfo_provenance missing_link ml_debiaser mobilebert model_pruning moe_models_implicit_bias moe_mtl moew mol_dqn mo...
At the same time, during the graph-based semi-supervised learning stage, similarity matrix is firstly adjusted through the latest learned sparse codes, and then is utilized to obtain a better classification function. To make the ISSC scale up to larger databases, a novel online dictionary ...
Thus, even when Sc and LapSc are combined, the class information is still missing, while DisScMM utilizes the class information effectively in its sparse code learning procedure. Download: Download full-size image Fig. 3. Comparison to ensemble sparse coding method. 3.2. Experiment II: Breast ...
P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004). Article PubMed Google Scholar Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15, 1090–1097 (2018)....
Detecting the underlying low-dimensional space (subspace) where high-dimensional data reside, is at the heart of several signal processing and machine learning tasks, such as network anomalies detection, [1], image denoising, [2], [3], direction of arrival (DOA) estimation, [4], etc. Batch...
In this paper, a novel similarity matrix construction methods are proposed which combined the high spectral correlation and rich spatial connection. Firstly, we utilize the cosine similarity of sparse representation vector to construct a novel similarity matrix. Then, the similarity matrix based on ...
In this work, we make one of the first attempts to employ structural similarity (SSIM) index, a more accurate perceptual image measure, by incorporating it into the framework of sparse signal representation and approximation. Specifically, the proposed optimization problem solves for coefficients with...