# Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection Source: [Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection]() Collection index: [llms.txt]() Data updated: 2026-01-04T05:14:57.913Z Authors: Mingtao Feng, Kendong Liu, Liang Zhang, Hongshan Yu, Yaonan Wang, Ajmal Mian Published: 2022-04-28T12:44:08.000Z ## Abstract Saliency detection with light field images is becoming attractive given the abundant cues available, however, this comes at the expense of large-scale pixel level annotated data which is expensive to generate. In this paper, we propose to learn light field saliency from pixel-level noisy labels obtained from unsupervised hand crafted featured based saliency methods. Given this goal, a natural question is: can we efficiently incorporate the relationships among light field cues while identifying clean labels in a unified framework? We address this question by formulating the learning as a joint optimization of intra light field features fusion stream and inter scenes correlation stream to generate the predictions. Specially, we first introduce a pixel forgetting guided fusion module to mutually enhance the light field features and exploit pixel consistency across iterations to identify noisy pixels. Next, we introduce a cross scene noise penalty loss for better reflecting latent structures of training data and enabling the learning to be invariant to noise. Extensive experiments on multiple benchmark datasets demonstrate the superiority of our framework showing that it learns saliency prediction comparable to state-of-the-art fully supervised light field saliency methods. Our code is available at https://github.com/OLobbCode/NoiseLF. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Saliency Detection on HFUT](): Saliency Detection; HFUT; F Score: 65.2. - [Saliency Detection on LFSD (F and M metrics)](): Saliency Detection; LFSD; F-Measure: 80.4. - [Saliency Detection on DUT-LF](): Saliency Detection; DUT-LF; F-measure: 81.3. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()