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- Hassan ForooshSuggested from co-authorship
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Works587 from public data
- A review of uncertainty quantification in deep learning: Techniques, applications and challenges2,893
This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning (RL), and outlines a few important applications of UZ methods.
- Single Image Dehazing via Multi-scale Convolutional Neural Networks1,759
A multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps by combining a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale network which refines results locally.
- Gated Fusion Network for Single Image Dehazing973
An efficient algorithm to directly restore a clear image from a hazy input using an end-to-end trainable neural network that consists of an encoder and a decoder is proposed.
- Single Image Super-Resolution via a Holistic Attention Network857
A new holistic attention network (HAN) is proposed, which consists of a layer attention module (LAM) and a channel-spatial attention Module (CSAM) to model the holistic interdependencies among layers, channels, and positions.
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- Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding506
This work introduces Chat-UniVi, a Unified Vision-language model capable of comprehending and engaging in conver-sations involving images and videos through a unified visual representation that outperforms even existing methods exclusively de-signed for either images or videos.
- Consistent and Specific Multi-View Subspace Clustering450
A novel multi-view subspace clustering method (CSMSC), where consistency and specificity are jointly exploited for subspace representation learning, and forms the multi-View self-representation property using a shared consistent representation and a set of specific representations, which better fits the real-world datasets.
- Cluster-Based Co-Saliency Detection427
This paper introduces a new cluster-based algorithm for co-saliency detection that is mostly bottom-up without heavy learning, and outperforms most the state-of-the-art saliency detection methods.
- Deep People Counting in Extremely Dense Crowds388
This paper proposes an end-to-end deep convolutional neural networks (CNN) regression model for counting people of images in extremely dense crowds that achieves superior performance than the state-of-the-arts in term of the mean and variance of absolute difference.
- Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges382
A multi-scale deep neural network for single image dehazing by learning the mapping between hazy images and their transmission maps by combining a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale network which refines dehazed results locally.
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- Single Image Deraining: A Comprehensive Benchmark Analysis371
A comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images, and provides a rich variety of criteria for dehazing algorithm evaluation.
- Depth Enhanced Saliency Detection Method365
This paper proposes a saliency detection method using the additional depth information and saliency cues are provided to follow the laws of the visually salient stimuli in both color and depth spaces.
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- Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images350
An end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs that significantly outperforms the existing state-of-the-art SOD competitors is proposed and a new and challenging optical RSI dataset is constructed that contains 2,000 images with pixel-wise saliency annotations.
- ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples340
An end-to-end image compression model to defend adversarial examples: ComDefend, which outperforms the state-of-the-art defense methods, and is consistently effective to protect classifiers against adversarial attacks.
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- Transferable Adversarial Attacks for Image and Video Object Detection280
The proposed method is based on the Generative Adversarial Network (GAN) framework, where it combines the high-level class loss and low-level feature loss to jointly train the adversarial example generator, and can efficiently generate image and video adversarial examples that have better transferability.
- Logit Standardization in Knowledge Distillation271
The proposed Z-score pre-process enables student to focus on essential logit relations from teacher rather than requiring a magnitude match, and can improve the performance of existing logit-based distillation methods.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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