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Found 11 papers, 3 papers with code
Towards Balanced Active Learning for Multimodal Classification
Our studies demonstrate that the proposed method achieves more balanced multimodal learning by avoiding greedy sample selection from the dominant modality.
UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning
Multimodal learning aims to imitate human beings to acquire complementary information from multiple modalities for various downstream tasks.
Speech Emotion Recognition with Co-Attention based Multi-level Acoustic Information
In this paper, we propose an end-to-end speech emotion recognition system using multi-level acoustic information with a newly designed co-attention module.
Are object detection assessment criteria ready for maritime computer vision?
However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting.
A learning-based approach for automatic image and video colorization
In this paper, we present a color transfer algorithm to colorize a broad range of gray images without any user intervention.
L1-regularized Reconstruction Error as Alpha Matte
Sampling-based alpha matting methods have traditionally followed the compositing equation to estimate the alpha value at a pixel from a pair of foreground (F) and background (B) samples.
Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection
First, the probabilistic contribution of each image region to the confidence of a CNN-based image classifier is computed through a backtracking strategy to produce top-down saliency.
Backtracking ScSPM Image Classifier for Weakly Supervised Top-Down Saliency
We propose a weakly supervised top-down saliency framework using only binary labels that indicate the presence/absence of an object in an image.
A Classifier-guided Approach for Top-down Salient Object Detection
Although the role of the classifier is to support salient object detection, we evaluate its performance in image classification and also illustrate the utility of thresholded saliency maps for image segmentation.
Sparse Coding for Alpha Matting
In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives the estimate of the alpha matte from a set of unpaired F and B samples.
Improving Image Matting Using Comprehensive Sampling Sets
The first is that the range in which the foreground and background are sampled is often limited to such an extent that the true foreground and background colors are not present.
