Saliency Detection
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Saliency Detection is a preprocessing step in computer vision which aims at finding salient objects in an image.
Source: An Unsupervised Game-Theoretic Approach to Saliency Detection
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/icvteam/pgnet
공식 코드Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
https://github.com/idip2025/cspenet
공식 코드CSPENet: Contour-Aware and Saliency Priors Embedding Network for Infrared Small Target Detection
https://github.com/idt-iti/ca-sum-360
공식 코드An Integrated System for Spatio-Temporal Summarization of 360-degrees Videos
https://github.com/ignatpolezhaev/mds-vitnet
공식 코드MDS-ViTNet: Improving saliency prediction for Eye-Tracking with Vision Transformer
https://github.com/imatge-upc/SalEMA
공식 코드Simple vs complex temporal recurrences for video saliency prediction
https://github.com/imatge-upc/salgan
공식 코드SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
https://github.com/imatge-upc/saliency-2016-cvpr
공식 코드Shallow and Deep Convolutional Networks for Saliency Prediction
https://github.com/imatge-upc/saliency-2016-lsun
공식 코드https://github.com/indigopurple/sasa
공식 코드SASA: Saliency-Aware Self-Adaptive Snapshot Compressive Imaging
https://github.com/intmegroup/avs-odv
공식 코드How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model
https://github.com/ispc-lab/RSKDD-Net
공식 코드RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
https://github.com/ivrl/ai4va
공식 코드Unlocking Comics: The AI4VA Dataset for Visual Understanding
https://github.com/ivrl/augsal
공식 코드Data Augmentation via Latent Diffusion for Saliency Prediction
https://github.com/ivrl/dissal
공식 코드https://github.com/ixilai/samf
공식 코드SAMF: Small-Area-Aware Multi-focus Image Fusion for Object Detection
https://github.com/jhl-det/rgbd_video_sod
공식 코드ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection
https://github.com/jingzhang617/cascaded_rgbd_sod
공식 코드RGB-D Saliency Detection via Cascaded Mutual Information Minimization
https://github.com/jingzhang617/ebmgsod
공식 코드https://github.com/jiwei0921/dsu
공식 코드Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
https://github.com/josephch405/jit-masker
공식 코드JIT-Masker: Efficient Online Distillation for Background Matting
https://github.com/kaviezhang/TexturedMeshSaliency
공식 코드Textured Mesh Saliency: Bridging Geometry and Texture for Human Perception in 3D Graphics
https://github.com/kaviezhang/meshmamba
공식 코드Mesh Mamba: A Unified State Space Model for Saliency Prediction in Non-Textured and Textured Meshes
https://github.com/kerenfu/LFSOD-Survey
공식 코드Light Field Salient Object Detection: A Review and Benchmark
https://github.com/knowledgetechnologyuhh/gasp
공식 코드https://github.com/kylemin/TASED-Net
공식 코드TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection
https://github.com/limuhit/vr-video-quality-in-the-wild
공식 코드Perceptual Quality Assessment of Virtual Reality Videos in the Wild
https://github.com/ljovo/transalnet
공식 코드TranSalNet: Towards perceptually relevant visual saliency prediction
https://github.com/luoyan407/congruency
공식 코드Direction Concentration Learning: Enhancing Congruency in Machine Learning
https://github.com/marcellacornia/mlnet
공식 코드https://github.com/marcellacornia/sam
공식 코드Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model
https://github.com/matthewkenely/framework-attention-news
공식 코드A Deep Learning Framework for Visual Attention Prediction and Analysis of News Interfaces
https://github.com/matthias-k/DeepGaze
공식 코드DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling
https://github.com/mbar0075/object-detection-correlation-saliency-vs-depth
공식 코드Correlation of Object Detection Performance with Visual Saliency and Depth Estimation
https://github.com/mjahanifar/mDRFI_matlab
공식 코드Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images
https://github.com/mobarakol/ST-MTL
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.