Stable signal recovery from incomplete and inaccurate measurements
Abstract Suppose we wish to recover a vector x 0 ∈ ℝ 𝓂 (e.g., a digital signal or image) from incomplete and contaminated observations y = A x 0 + e ; A is an 𝓃 × 𝓂 matrix wi...
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309 publications
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing...
Atomic Decomposition by Basis Pursuit
The time-frequency and time-scale communities have recently developed a large number of overcomplete waveform dictionaries --- stationary wavelets, wavelet packets, cosine packe...
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Suppose we are given a vector f in a class F ⊂ ℝN, e.g., a class of digital signals or digital images. How many linear measurements do we need to make about f to be able to reco...
Segment Anything
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest ...
Statistical pattern recognition: a review
The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated...
Dual Attention Network for Scene Segmentation
In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the self-attention mechanism. Unlike previous works that capture context...
Video Google: a text retrieval approach to object matching in videos
We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a s...
Dynamic Graph CNN for Learning on Point Clouds
Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition ...
Image Style Transfer Using Convolutional Neural Networks
Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack ...