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- Visual Saliency Transformer547
This work develops a novel unified model based on a pure transformer, namely, Visual Saliency Transformer, for both RGB and RGB-D salient object detection (SOD), which takes image patches as inputs and leverages the transformer to propagate global contexts among image patches.
- Salient Object Detection via Integrity Learning480
This work designs a novel integrity learning for SOD, which outperforms the baseline methods in terms of a wide range of metrics and introduces an integrity channel enhancement (ICE) component with the goal of enhancing feature channels that highlight the integral salient objects, while suppressing the other distracting ones.
- CNNs-Based RGB-D Saliency Detection via Cross-View Transfer and Multiview Fusion423
A novel framework based on convolutional neural networks (CNNs), which transfers the structure of the RGB-based deep neural network to be applicable for depth view and fuses the deep representations of both views automatically to obtain the final saliency map is proposed.
- Online Multi-Object Tracking with Dual Matching Attention Networks374
This paper introduces a cost-sensitive tracking loss based on the state-of-the-art visual tracker which encourages the model to focus on hard negative distractors during online learning and proposes Dual Matching Attention Networks (DMAN) with both spatial and temporal attention mechanisms.
- Context-aware Cross-level Fusion Network for Camouflaged Object Detection347
A novel Context-aware Cross-level Fusion Network (C2F-Net) to address the challenging COD task and proposes an Attention-induced Cross- level Fusion Module (ACFM) to integrate the multi-level features with informative attention coefficients.
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- A Deep Spatial Contextual Long-Term Recurrent Convolutional Network for Saliency Detection207
The proposed DSCLSTM model can significantly boost the saliency detection performance by incorporating both global spatial interconnections and scene context modulation, which may uncover novel inspirations for studies on them in computational saliency models.
- Learning Non-target Knowledge for Few-shot Semantic Segmentation166
A novel framework, namely Non-Target Region Eliminating (NTRE) network, to explicitly mine and eliminate BG and DO regions in the query to improve the model ability of distinguishing the target object from DOs.
- PiCANet: Pixel-Wise Contextual Attention Learning for Accurate Saliency Detection144
This work introduces a novel pixel-wise contextual attention network (PiCANet) to selectively attend to informative context locations at each pixel and introduces the proposed PiCANets into a U-Net model for salient object detection.
- Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection128
A novel mutual attention model is proposed that uses the non-local attention of one modality to propagate long-range contextual dependencies for the other, thus leveraging complementary attention cues to achieve high-order and trilinear cross-modal interaction.
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- Learning to Predict Eye Fixations via Multiresolution Convolutional Neural Networks110
A novel computation model to predict eye fixations, which adopts a multiresolution convolutional neural network (Mr-CNN) to infer these three types of saliency cues from raw image data simultaneously, demonstrating the superiority of Mr-CNN.
- Weakly Supervised Video Salient Object Detection89
An "Appearance-motion fusion module" and bidirectional ConvLSTM based framework are proposed to achieve effective multi-modal learning and long-term temporal context modeling based on the first weakly supervised video salient object detection model based on relabeled “fixation guided scribble annotations”.
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- Spatiotemporal Convolutional Neural Network with Convolutional Block Attention Module for Micro-Expression Recognition77
The experimental results show that the 3D CNN with convolutional block attention module outperformed other algorithms in micro-expression recognition.
- Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection76
A novel consensus-aware dynamic convolution model is proposed to explicitly and effectively perform the "summarize and search" process to summarize consensus image features and generate dynamic kernels from consensus features to encode the summarized consensus knowledge.
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- FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering69
This paper proposes a unified framework, FiDeLiS, designed to improve the factuality of LLM responses by anchoring answers to verifiable reasoning steps retrieved from KGs, and leverages step-wise beam search with a deductive scoring function.
- VST++: Efficient and Stronger Visual Saliency Transformer67
The authors' VST++ model outperforms existing methods while achieving a 25% reduction in computational costs without significant performance compromise, and is evaluated across various transformer-based backbones on RGB, RGB-D, and RGB-T SOD benchmark datasets.
- Latent source mining in FMRI via restricted Boltzmann machine62
The proposed method not only interprets fMRI time courses explicitly to take advantages of deep learning models in latent feature learning but also substantially reduces model complexity and increases the scale of training set to improve training efficiency.
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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