Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning

Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an economical alternative, with which training phase could hardly generate satisfactory performance unfortunately. In order to generate high-quality annotated data with a low time cost for accurate segmentation, in this paper, we propose a novel annotation enrichment strategy, which expands existing coarse annotations of training data to a finer scale. Extensive experiments on the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural networks trained with the enriched annotations from our framework yield a significant improvement over that trained with the original coarse labels. It is highly competitive to the performance obtained by using human annotated dense annotations. The proposed method also outperforms among other state-of-the-art weakly-supervised segmentation methods.Comment: CIKM 2018 International Conference on Information and Knowledge Managemen

Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search

Mobile landmark search (MLS) recently receives increasing attention for its great practical values. However, it still remains unsolved due to two important challenges. One is high bandwidth consumption of query transmission, and the other is the huge visual variations of query images sent from mobile devices. In this paper, we propose a novel hashing scheme, named as canonical view based discrete multi-modal hashing (CV-DMH), to handle these problems via a novel three-stage learning procedure. First, a submodular function is designed to measure visual representativeness and redundancy of a view set. With it, canonical views, which capture key visual appearances of landmark with limited redundancy, are efficiently discovered with an iterative mining strategy. Second, multi-modal sparse coding is applied to transform visual features from multiple modalities into an intermediate representation. It can robustly and adaptively characterize visual contents of varied landmark images with certain canonical views. Finally, compact binary codes are learned on intermediate representation within a tailored discrete binary embedding model which preserves visual relations of images measured with canonical views and removes the involved noises. In this part, we develop a new augmented Lagrangian multiplier (ALM) based optimization method to directly solve the discrete binary codes. We can not only explicitly deal with the discrete constraint, but also consider the bit-uncorrelated constraint and balance constraint together. Experiments on real world landmark datasets demonstrate the superior performance of CV-DMH over several state-of-the-art methods

Robust discrete code modeling for supervised hashing

Recent years have witnessed the promising efficacy and efficiency of hashing (also known as binary code learning) for retrieving nearest neighbor in large-scale data collections. Particularly, with supervision knowledge (e.g., semantic labels), we may further gain considerable performance boost. Nevertheless, most existing supervised hashing schemes suffer from the following limitations: (1) severe quantization error caused by continuous relaxation of binary codes; (2) disturbance of unreliable codes in subsequent hash function learning; and (3) erroneous guidance derived from imprecise and incomplete semantic labels. In this work, we propose a novel supervised hashing approach, termed as Robust Discrete Code Modeling (RDCM), which directly learns high-quality discrete binary codes and hash functions by effectively suppressing the influence of unreliable binary codes and potentially noisily-labeled samples. RDCM employs ℓ norm, which is capable of inducing sample-wise sparsity, to jointly perform code selection and noisy sample identification. Moreover, we preserve the discrete constraint in RDCM to eliminate the quantization error. An efficient algorithm is developed to solve the discrete optimization problem. Extensive experiments conducted on various real-life datasets show the superiority of the proposed RDCM approach as compared to several state-of-the-art hashing methods