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Found 24 papers, 4 papers with code
Rethinking Lightweight Salient Object Detection via Network Depth-Width Tradeoff
To this end, we design a lightweight framework while maintaining satisfying competitive accuracy.
Part-guided Relational Transformers for Fine-grained Visual Recognition
This framework, namely PArt-guided Relational Transformers (PART), is proposed to learn the discriminative part features with an automatic part discovery module, and to explore the intrinsic correlations with a feature transformation module by adapting the Transformer models from the field of natural language processing.
Ranked #4 on
Fine-Grained Image Classification
on FGVC Aircraft
Fine-Grained Image Classification
Fine-Grained Visual Recognition
+1
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically, guided by different source feature during decoding process.
Ranked #4 on
RGB Salient Object Detection
on UHRSD
(using extra training data)
Receptive Field Broadening and Boosting for Salient Object Detection
Moreover, Unlike multi-path parallel training, MHB randomly selects one branch each time for gradient back propagation in a boosting way.
RGB-D Salient Object Detection with Ubiquitous Target Awareness
To construct our framework as well as achieving accurate salient detection results, we propose a Ubiquitous Target Awareness (UTA) network to solve three important challenges in RGB-D SOD task: 1) a depth awareness module to excavate depth information and to mine ambiguous regions via adaptive depth-error weights, 2) a spatial-aware cross-modal interaction and a channel-aware cross-level interaction, exploiting the low-level boundary cues and amplifying high-level salient channels, and 3) a gated multi-scale predictor module to perceive the object saliency in different contextual scales.
Is Depth Really Necessary for Salient Object Detection?
To solve this, many recent RGBD-based networks are proposed by adopting the depth map as an independent input and fuse the features with RGB information.
Ranked #1 on
RGB-D Salient Object Detection
on RGBD135
Single Image Dehazing Using Ranking Convolutional Neural Network
To obtain effective features for single image dehazing, this paper presents a novel Ranking Convolutional Neural Network (Ranking-CNN).
Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes
For the task of mobility analysis of 3D shapes, we propose joint analysis for simultaneous motion part segmentation and motion attribute estimation, taking a single 3D model as input.
Complementary Segmentation of Primary Video Objects with Reversible Flows
Segmenting primary objects in a video is an important yet challenging problem in computer vision, as it exhibits various levels of foreground/background ambiguities.
Learning to Group and Label Fine-Grained Shape Components
We propose to generate part hypotheses from the components based on a hierarchical grouping strategy, and perform labeling on those part groups instead of directly on the components.
Image Co-segmentation via Multi-scale Local Shape Transfer
Image co-segmentation is a challenging task in computer vision that aims to segment all pixels of the objects from a predefined semantic category.
Primary Video Object Segmentation via Complementary CNNs and Neighborhood Reversible Flow
By applying CCNN on each video frame, the spatial foregroundness and backgroundness maps can be initialized, which are then propagated between various frames so as to segment primary video objects and suppress distractors.
Look, Perceive and Segment: Finding the Salient Objects in Images via Two-Stream Fixation-Semantic CNNs
Toward this end, this paper proposes two-stream fixation-semantic CNNs, whose architecture is inspired by the fact that salient objects in complex images can be unambiguously annotated by selecting the pre-segmented semantic objects that receive the highest fixation density in eye-tracking experiments.
Embedding 3D Geometric Features for Rigid Object Part Segmentation
One stream, named AppNet, is to extract 2D appearance features from the input image.
What Is and What Is Not a Salient Object? Learning Salient Object Detector by Ensembling Linear Exemplar Regressors
Finding what is and what is not a salient object can be helpful in developing better features and models in salient object detection (SOD).
A Benchmark Dataset and Saliency-guided Stacked Autoencoders for Video-based Salient Object Detection
Based on this dataset, this paper proposes an unsupervised baseline approach for video-based SOD by using saliency-guided stacked autoencoders.
Conformal and Low-Rank Sparse Representation for Image Restoration
In this paper, we propose a novel sparse representation approach called conformal and low-rank sparse representation (CLRSR) for image restoration problems.
Video Matting via Sparse and Low-Rank Representation
We introduce a novel method of video matting via sparse and low-rank representation.
A Data-Driven Metric for Comprehensive Evaluation of Saliency Models
To address this problem, we propose a data-driven metric for comprehensive evaluation of saliency models.
Semantic Object Segmentation via Detection in Weakly Labeled Video
Semantic object segmentation in video is an important step for large-scale multimedia analysis.
Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance
With this graph representation, we pose trajectory analysis as a joint task of spatial graph partitioning and temporal graph matching.
Sparse Dictionary Learning for Edit Propagation of High-Resolution Images
Previous approaches for edit propagation typically employ a global optimization over the whole set of image pixels, incurring a prohibitively high memory and time consumption for high-resolution images.
Video Editing with Temporal, Spatial and Appearance Consistency
Given an area of interest in a video sequence, one may want to manipulate or edit the area, e. g. remove occlusions from or replace with an advertisement on it.
Image Matting with Local and Nonlocal Smooth Priors
This nonlocal smooth prior and the well known local smooth prior from matting Laplacian complement each other.
