TL-ADA: Transferable Loss-based Active Domain Adaptation.

The field of Active Domain Adaptation (ADA) has been investigating ways to close the performance gap between supervised and unsupervised learning settings. Previous ADA research has primarily focused on query selection, but there has been little examination of how to effectively train newly labeled target samples using both labeled source samples and unlabeled target samples. In this study, we present a novel Transferable Loss-based ADA (TL-ADA) framework. Our approach is inspired by loss-based query selection, which has shown promising results in active learning. However, directly applying loss-based query selection to the ADA scenario leads to a buildup of high-loss samples that do not contribute to the model due to transferability issues and low diversity. To address these challenges, we propose a transferable doubly nested loss, which incorporates target pseudo labels and a domain adversarial loss. Our TL-ADA framework trains the model sequentially, considering both the domain type (source/target) and the availability of labels (labeled/unlabeled). Additionally, we encourage the pseudo labels to have low self-entropy and diverse class distributions to improve their reliability. Experiments on several benchmark datasets demonstrate that our TL-ADA model outperforms previous ADA methods, and in-depth analysis supports the effectiveness of our proposed approach.

Unified Simultaneous Clustering and Feature Selection for Unlabeled and Labeled Data.

This paper proposes a novel feature selection method, namely, unified simultaneous clustering feature selection (USCFS). A regularized regression with a new type of target matrix is formulated to select the most discriminative features among the original features from labeled or unlabeled data. The regression with -norm regularization allows the projection matrix to represent an effective selection of discriminative features. For unsupervised feature selection, the target matrix discovers label-like information not from the original data points but rather from projected data points, which are of a reduced dimensionality. Without the aid of an affinity graph-based local structure learning method, USCFS allows the target matrix to capture latent cluster centers via orthogonal basis clustering and to simultaneously select discriminative features guided by latent cluster centers. When class labels are available, the target matrix is also able to find latent class labels by regarding the ground-truth class labels as an approximate guide. Hence, supervised feature selection is realized using these latent class labels, which may differ from the ground-truth class labels. Experimental results demonstrate the effectiveness of the proposed method. Specifically, the proposed method outperforms the state-of-the-art methods on diverse real-world data sets for both the supervised and the unsupervised feature selection.

Salient Region Detection via High-Dimensional Color Transform and Local Spatial Support.

In this paper, we introduce a novel approach to automatically detect salient regions in an image. Our approach consists of global and local features, which complement each other to compute a saliency map. The first key idea of our work is to create a saliency map of an image by using a linear combination of colors in a high-dimensional color space. This is based on an observation that salient regions often have distinctive colors compared with backgrounds in human perception, however, human perception is complicated and highly nonlinear. By mapping the low-dimensional red, green, and blue color to a feature vector in a high-dimensional color space, we show that we can composite an accurate saliency map by finding the optimal linear combination of color coefficients in the high-dimensional color space. To further improve the performance of our saliency estimation, our second key idea is to utilize relative location and color contrast between superpixels as features and to resolve the saliency estimation from a trimap via a learning-based algorithm. The additional local features and learning-based algorithm complement the global estimation from the high-dimensional color transform-based algorithm. The experimental results on three benchmark datasets show that our approach is effective in comparison with the previous state-of-the-art saliency estimation methods.