Search Results for author:
Found 59 papers, 54 papers with code
BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network
In particular, we 1) propose a bifurcated backbone strategy (BBS) to split the multi-level features into teacher and student features, and 2) utilize a depth-enhanced module (DEM) to excavate informative parts of depth cues from the channel and spatial views.
QR-CLIP: Introducing Explicit Open-World Knowledge for Location and Time Reasoning
Daily images may convey abstract meanings that require us to memorize and infer profound information from them.
CamoFormer: Masked Separable Attention for Camouflaged Object Detection
How to identify and segment camouflaged objects from the background is challenging.
Source-free Depth for Object Pop-out
In this work, we adapt such depth inference models for object segmentation using the objects' ``pop-out'' prior in 3D.
Masked Vision-Language Transformer in Fashion
We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation.
OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers
We present OSFormer, the first one-stage transformer framework for camouflaged instance segmentation (CIS).
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes.
Ranked #1 on
Co-Salient Object Detection
on CoSal2015
Deep Gradient Learning for Efficient Camouflaged Object Detection
This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD).
Towards Deeper Understanding of Camouflaged Object Detection
With the above understanding about camouflaged objects, we present the first triple-task learning framework to simultaneously localize, segment, and rank camouflaged objects, indicating the conspicuousness level of camouflage.
Video Polyp Segmentation: A Deep Learning Perspective
We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era.
Ranked #1 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Implicit Motion Handling for Video Camouflaged Object Detection
We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames.
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 #3 on
Dichotomous Image Segmentation
on DIS-TE4
Facial-Sketch Synthesis: A New Challenge
Besides, we elaborate comprehensive experiments on the existing 19 cutting-edge models.
Weakly Supervised Visual-Auditory Fixation Prediction with Multigranularity Perception
Moreover, we distill knowledge from these regions to obtain complete new spatial-temporal-audio (STA) fixation prediction (FP) networks, enabling broad applications in cases where video tags are not available.
Dense Uncertainty Estimation
Deep neural networks can be roughly divided into deterministic neural networks and stochastic neural networks. The former is usually trained to achieve a mapping from input space to output space via maximum likelihood estimation for the weights, which leads to deterministic predictions during testing.
IDENTIFYING CONCEALED OBJECTS FROM VIDEOS
The proposed SLT-Net leverages on both short-term dynamics and long-term temporal consistency to detect concealed objects in continuous video frames.
RGB-D Saliency Detection via Cascaded Mutual Information Minimization
In this paper, we introduce a novel multi-stage cascaded learning framework via mutual information minimization to "explicitly" model the multi-modal information between RGB image and depth data.
Specificity-preserving RGB-D Saliency Detection
To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information.
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers
Unlike existing CNN-based methods, we adopt a transformer encoder, which learns more powerful and robust representations.
Ranked #5 on
Medical Image Segmentation
on CVC-ColonDB
Full-Duplex Strategy for Video Object Segmentation
Previous video object segmentation approaches mainly focus on using simplex solutions between appearance and motion, limiting feature collaboration efficiency among and across these two cues.
Ranked #6 on
Video Polyp Segmentation
on SUN-SEG-Hard (Unseen)
PVT v2: Improved Baselines with Pyramid Vision Transformer
We hope this work will facilitate state-of-the-art Transformer researches in computer vision.
Ranked #60 on
Object Detection
on COCO minival
From Semantic Categories to Fixations: A Novel Weakly-Supervised Visual-Auditory Saliency Detection Approach
Thanks to the rapid advances in the deep learning techniques and the wide availability of large-scale training sets, the performances of video saliency detection models have been improving steadily and significantly.
Probabilistic Model Distillation for Semantic Correspondence
We address this problem with the use of a novel Probabilistic Model Distillation (PMD) approach which transfers knowledge learned by a probabilistic teacher model on synthetic data to a static student model with the use of unlabeled real image pairs.
Progressively Normalized Self-Attention Network for Video Polyp Segmentation
Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features.
Ranked #5 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Salient Objects in Clutter
This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets.
Camouflaged Object Segmentation with Distraction Mining
In this paper, we strive to embrace challenges towards effective and efficient COS. To this end, we develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature.
Ranked #2 on
Camouflaged Object Segmentation
on CAMO
Camouflaged Object Segmentation
Dichotomous Image Segmentation
+1
Generative Transformer for Accurate and Reliable Salient Object Detection
For the former, we apply transformer to a deterministic model, and explain that the effective structure modeling and global context modeling abilities lead to its superior performance compared with the CNN based frameworks.
Mutual Graph Learning for Camouflaged Object Detection
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models.
Kaleido-BERT: Vision-Language Pre-training on Fashion Domain
We present a new vision-language (VL) pre-training model dubbed Kaleido-BERT, which introduces a novel kaleido strategy for fashion cross-modality representations from transformers.
Group Collaborative Learning for Co-Salient Object Detection
We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module; 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module conditioning the inconsistent consensus.
Ranked #4 on
Co-Salient Object Detection
on CoSOD3k
Simultaneously Localize, Segment and Rank the Camouflaged Objects
With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects.
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Unlike the recently-proposed Transformer model (e. g., ViT) that is specially designed for image classification, we propose Pyramid Vision Transformer~(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks.
Ranked #5 on
Semantic Segmentation
on SynPASS
Concealed Object Detection
We present the first systematic study on concealed object detection (COD), which aims to identify objects that are "perfectly" embedded in their background.
Ranked #1 on
Camouflaged Object Segmentation
on CAMO
(using extra training data)
Camouflaged Object Segmentation
Dichotomous Image Segmentation
+1
Salient Object Detection via Integrity Learning
We define the concept of integrity at both a micro and macro level.
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.
Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection
Spotting objects that are visually adapted to their surroundings is challenging for both humans and AI.
Light Field Salient Object Detection: A Review and Benchmark
Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models, are achieved.
Uncertainty Inspired RGB-D Saliency Detection
Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution.
Ranked #1 on
RGB Salient Object Detection
on DUTS-test
(MAE metric)
RGB-D Salient Object Detection
RGB Salient Object Detection
+1
Siamese Network for RGB-D Salient Object Detection and Beyond
Inspired by the observation that RGB and depth modalities actually present certain commonality in distinguishing salient objects, a novel joint learning and densely cooperative fusion (JL-DCF) architecture is designed to learn from both RGB and depth inputs through a shared network backbone, known as the Siamese architecture.
Ranked #2 on
RGB-D Salient Object Detection
on STERE
RGB-D Salient Object Detection: A Survey
Further, considering that the light field can also provide depth maps, we review SOD models and popular benchmark datasets from this domain as well.
Re-thinking Co-Salient Object Detection
CoSOD is an emerging and rapidly growing extension of salient object detection (SOD), which aims to detect the co-occurring salient objects in a group of images.
Ranked #6 on
Co-Salient Object Detection
on CoCA
Bifurcated backbone strategy for RGB-D salient object detection
In particular, first, we propose to regroup the multi-level features into teacher and student features using a bifurcated backbone strategy (BBS).
Ranked #1 on
RGB-D Salient Object Detection
on NJU2K
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images.
Ranked #4 on
Camouflaged Object Segmentation
on CAMO
(using extra training data)
Camouflaged Object Detection
We present a comprehensive study on a new task named camouflaged object detection (COD), which aims to identify objects that are "seamlessly" embedded in their surroundings.
Ranked #2 on
Camouflaged Object Segmentation
on COD
Taking a Deeper Look at Co-Salient Object Detection
Co-salient object detection (CoSOD) is a newly emerging and rapidly growing branch of salient object detection (SOD), which aims to detect the co-occurring salient objects in multiple images.
Ranked #2 on
Co-Salient Object Detection
on iCoSeg
Bilateral Attention Network for RGB-D Salient Object Detection
To better explore salient information in both foreground and background regions, this paper proposes a Bilateral Attention Network (BiANet) for the RGB-D SOD task.
Ranked #3 on
RGB-D Salient Object Detection
on RGBD135
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis.
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection.
Ranked #6 on
RGB-D Salient Object Detection
on NLPR
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity.
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.
Ranked #4 on
RGB-D Salient Object Detection
on LFSD
Scoot: A Perceptual Metric for Facial Sketches
In this paper, we design a perceptual metric, called Structure Co-Occurrence Texture (Scoot), which simultaneously considers the block-level spatial structure and co-occurrence texture statistics.
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
The use of RGB-D information for salient object detection has been extensively explored in recent years.
Ranked #4 on
RGB-D Salient Object Detection
on RGBD135
Shifting More Attention to Video Salient Object Detection
This is the first work that explicitly emphasizes the challenge of saliency shift, i. e., the video salient object(s) may dynamically change.
Ranked #1 on
Video Salient Object Detection
on DAVSOD-Difficult20
LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer
We design a novel Local Spatial Aware (LSA) layer, which can learn to generate Spatial Distribution Weights (SDWs) hierarchically based on the spatial relationship in local region for spatial independent operations, to establish the relationship between these operations and spatial distribution, thus capturing the local geometric structure sensitively. We further propose the LSANet, which is based on LSA layer, aggregating the spatial information with associated features in each layer of the network better in network design. The experiments show that our LSANet can achieve on par or better performance than the state-of-the-art methods when evaluating on the challenging benchmark datasets.
Enhanced-alignment Measure for Binary Foreground Map Evaluation
The existing binary foreground map (FM) measures to address various types of errors in either pixel-wise or structural ways.
Face Sketch Synthesis Style Similarity:A New Structure Co-occurrence Texture Measure
However, human perception of the similarity of two sketches will consider both structure and texture as essential factors and is not sensitive to slight ("pixel-level") mismatches.
Semantic Edge Detection with Diverse Deep Supervision
Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition.
Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground
Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter.
Structure-measure: A New Way to Evaluate Foreground Maps
Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a SM and a GT map.
