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Found 13 papers, 9 papers with code
Reconstructing Visual Stimulus Images from EEG Signals Based on Deep Visual Representation Model
Considering the advantages of low cost and easy portability of the electroencephalogram (EEG) acquisition equipments, we propose a novel image reconstruction method based on EEG signals in this paper.
DC-Net: Divide-and-Conquer for Salient Object Detection
In this paper, we introduce Divide-and-Conquer into the salient object detection (SOD) task to enable the model to learn prior knowledge that is for predicting the saliency map.
High-resolution Iterative Feedback Network for Camouflaged Object Detection
Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings.
Highly Accurate Dichotomous Image Segmentation
We present a systematic study on a new task called dichotomous image segmentation (DIS) , which aims to segment highly accurate objects from natural images.
Ranked #4 on
Dichotomous Image Segmentation
on DIS-TE1
Facial-Sketch Synthesis: A New Challenge
Besides, we elaborate comprehensive experiments on the existing 19 cutting-edge models.
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model
Multi-label emotion classification is an important task in NLP and is essential to many applications.
Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels
This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations.
Boundary-Aware Segmentation Network for Mobile and Web Applications
In this paper, we propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet), which comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation.
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD).
Ranked #1 on
Salient Object Detection
on SOD
Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation
Most of the existing methods treat this task as a problem of single-label multi-class text classification.
BASNet: Boundary-Aware Salient Object Detection
In this paper, we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection.
Ranked #3 on
RGB Salient Object Detection
on SOC
Camouflaged Object Segmentation
Dichotomous Image Segmentation
+5
Incremental 3D Line Segment Extraction from Semi-dense SLAM
This approach reduces a 3D line segment fitting problem into two 2D line segment fitting problems and takes advantage of both images and depth maps.
Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping
The tracking scheme is coherently integrated into a perceptual grouping framework in which the visual tracking problem is tackled by identifying a subset of these line segments and connecting them sequentially to form a closed boundary with the largest saliency and a certain similarity to the previous one.
