Many object instance retrieval systems are typically based on matching of local features, such as SIFT. However, these local descriptors serve as low-level clues, which are not sufficiently distinctive to prevent false matches. Recently, deep convolutional neural networks (CNN) have shown their promise as a semantic-aware representation for many computer vision tasks. In this paper, we propose a novel approach to employ CNN evidences to improve the SIFT matching accuracy, which plays a critical role in improving the object retrieval performance. To weaken the interference of noise, we extract compact CNN representations from a number of generic object regions. Then a query-adaptive method is proposed to choose appropriate CNN evidence to verify each pre-matched SIFT pair. Two different visual matching verification functions are introduced and evaluated. Moreover, we investigate the suitability of fine-tuning the CNN for our proposed approach. Extensive experiments on benchmark dataSets demonstrate the effectiveness of our method for particular object retrieval. Our results compare favorably to the state-of-the-art methods with acceptable memory usage and query time. (C) 2017 Elsevier B.V. All rights reserved.