Sentence examples for recognition benchmark from inspiring English sources

We describe the approach that won the final phase of the German traffic sign recognition benchmark.

Lin et al. employ B-CNN [8] to achieve remarkable performance in several fine-grained visual recognition benchmark datasets.

We used the training dataset provided by the "REVERB challenge" (reverberant voice enhancement and recognition benchmark) [40].

The recently released REverberant Voice Enhancement and Recognition Benchmark (REVERB) challenge includes a reverberant automatic speech recognition (ASR) task.

We describe our acoustic modeling method that sets a new recognition benchmark on AphasiaBank, a large-scale aphasic speech corpus.

The proposed method is evaluated using two publicly available datasets such as, Belgium traffic sign classification (BTSC) and German traffic sign recognition benchmark (GTSRB).

This database is often used in phone recognition benchmarking, e.g.[5, 7, 30].

State-of-the-art results are achieved on several well-known face recognition benchmarks.

While other image recognition benchmarks may rely on millions of photos that human beings have pored through and annotated personally, Facebook had to find methods to clean up what users had submitted that they could do at scale.

Based on the discriminative and compact framework, we achieved the state-of-the-art performance in terms of classification accuracy on several fine-grained image recognition benchmarks based on weekly supervision.

Focusing on the classes of different object recognition benchmarks (PASCAL VOC 2012, Caltech-256, ImageNet, GRAZ and OxfordPet), our approach collects novel training images, compared to the ones obtained by exploiting Google Images with the simple text class-label.