Search Results for author:
Found 70 papers, 27 papers with code
Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning
Building on this insight, we propose a novel semantic drift calibration method that incorporates mean shift compensation and covariance calibration.
HV-BEV: Decoupling Horizontal and Vertical Feature Sampling for Multi-View 3D Object Detection
Additionally, instead of relying on uniform sampling within a fixed height range, we introduce a height-aware module that incorporates historical information, enabling the reference points to adaptively focus on the varying heights at which objects appear in different scenes.
CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction
3D Gaussian Splatting (3DGS) has demonstrated impressive performance in scene reconstruction.
Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection
To this end, inspired by the agreeable nature of binary segmentation for SOD and COD, we propose a Contrastive Distillation Paradigm (CDP) to distil the foreground from the background, facilitating the identification of salient and camouflaged objects amidst their surroundings.
PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model
PolSAR data presents unique challenges due to its rich and complex characteristics.
DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes
This paper presents DGTR, a novel distributed framework for efficient Gaussian reconstruction for sparse-view vast scenes.
Mamba Capsule Routing Towards Part-Whole Relational Camouflaged Object Detection
These type-level mamba capsules are fed into the EM routing algorithm to get the high-layer mamba capsules, which greatly reduce the computation and parameters caused by the pixel-level capsule routing for part-whole relationships exploration.
Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement
Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge.
CONDA: Condensed Deep Association Learning for Co-Salient Object Detection
They directly rely on raw associations which are not reliable in complex scenarios, and their image feature optimization approach is not explicit for inter-image association modeling.
ASPS: Augmented Segment Anything Model for Polyp Segmentation
Polyp segmentation plays a pivotal role in colorectal cancer diagnosis.
XLD: A Cross-Lane Dataset for Benchmarking Novel Driving View Synthesis
To address this, this paper presents a novel driving view synthesis dataset and benchmark specifically designed for autonomous driving simulations.
VDG: Vision-Only Dynamic Gaussian for Driving Simulation
Dynamic Gaussian splatting has led to impressive scene reconstruction and image synthesis advances in novel views.
Center-Sensitive Kernel Optimization for Efficient On-Device Incremental Learning
Based on an empirical study on the knowledge intensity of the kernel elements of the neural network, we find that the center kernel is the key for maximizing the knowledge intensity for learning new data, while freezing the other kernel elements would get a good balance on the model's capacity for overcoming catastrophic forgetting.
Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports
The absence of adequately sufficient expert-level tumor annotations hinders the effectiveness of supervised learning based opportunistic cancer screening on medical imaging.
Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation
Experimental evaluations conducted on MS COCO, Cityscapes, and CTW1500 datasets indicate that the QEIS models' performance can be significantly improved when pre-trained with our method.
AUG: A New Dataset and An Efficient Model for Aerial Image Urban Scene Graph Generation
To fill in the gap of the overhead view dataset, this paper constructs and releases an aerial image urban scene graph generation (AUG) dataset.
GGRt: Towards Pose-free Generalizable 3D Gaussian Splatting in Real-time
Specifically, we design a novel joint learning framework that consists of an Iterative Pose Optimization Network (IPO-Net) and a Generalizable 3D-Gaussians (G-3DG) model.
Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure
It considers the characteristics of the image restoration task with multiple degenerations in continual learning, and the knowledge for different degenerations can be shared and accumulated in the unified network structure.
SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images
To eliminate the reliance on such priors, we propose a novel Structure-aware Mixup and Invariance Learning framework (SA-MixNet) for weakly supervised road extraction that improves the model invariance in a data-driven manner.
Revisiting the Power of Prompt for Visual Tuning
Inspired by the observation that the prompt tokens tend to share high mutual information with patch tokens, we propose initializing prompts with downstream token prototypes.
Ranked #1 on
Visual Prompt Tuning
on VTAB-1k(Structured<8>)
VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning
Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks.
GP-NeRF: Generalized Perception NeRF for Context-Aware 3D Scene Understanding
Applying NeRF to downstream perception tasks for scene understanding and representation is becoming increasingly popular.
SegGPT Meets Co-Saliency Scene
Co-salient object detection targets at detecting co-existed salient objects among a group of images.
Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation
A surge of interest has emerged in weakly supervised semantic segmentation due to its remarkable efficiency in recent years.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Revisiting Long-tailed Image Classification: Survey and Benchmarks with New Evaluation Metrics
We set up novel evaluation benchmarks based on a series of testing sets with evolving distributions.
Ranked #66 on
Long-tail Learning
on CIFAR-100-LT (ρ=100)
Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt
Inspired by the recent success of the Prompting technique, we introduce a new pre-training method that boosts QEIS models by giving Saliency Prompt for queries/kernels.
Compound Batch Normalization for Long-tailed Image Classification
It can model the feature space more comprehensively and reduce the dominance of head classes.
Combating Noisy Labels in Long-Tailed Image Classification
Most existing methods that cope with noisy labels usually assume that the class distributions are well balanced, which has insufficient capacity to deal with the practical scenarios where training samples have imbalanced distributions.
Deep 3D Vessel Segmentation based on Cross Transformer Network
In CTN, a transformer module is constructed in parallel to a U-Net to learn long-distance dependencies between different anatomical regions; and these dependencies are communicated to the U-Net at multiple stages to endow it with global awareness.
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance
Although deep learning algorithms have been intensively developed for computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully annotated datasets, leading to much time and resource consumption.
Structured Attention Composition for Temporal Action Localization
To tackle this issue, we make an early effort to study temporal action localization from the perspective of multi-modality feature learning, based on the observation that different actions exhibit specific preferences to appearance or motion modality.
Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction
To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios.
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution
Improving the resolution of magnetic resonance (MR) image data is critical to computer-aided diagnosis and brain function analysis.
Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation
Semantic segmentation with limited annotations, such as weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS), is a challenging task that has attracted much attention recently.
Ranked #35 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Colar: Effective and Efficient Online Action Detection by Consulting Exemplars
Based on the exemplar-consultation mechanism, the long-term dependencies can be captured by regarding historical frames as exemplars, while the category-level modeling can be achieved by regarding representative frames from a category as exemplars.
Ranked #6 on
Online Action Detection
on TVSeries
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation
Recent advances in machine learning and prevalence of digital medical images have opened up an opportunity to address the challenging brain tumor segmentation (BTS) task by using deep convolutional neural networks.
Robust Region Feature Synthesizer for Zero-Shot Object Detection
Zero-shot object detection aims at incorporating class semantic vectors to realize the detection of (both seen and) unseen classes given an unconstrained test image.
Ranked #2 on
Zero-Shot Object Detection
on PASCAL VOC'07
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis
Under this circumstance, the models learned from different views can distill valuable knowledge to guide the learning processes of each other.
Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image Recognition
Then, a Teacher-Assistant-Student (TAS) framework is further established to disentangle pixel distillation into the model compression stage and input compression stage, which significantly reduces the overall complexity of pixel distillation and the difficulty of distilling intermediate knowledge.
Weakly Supervised Semantic Segmentation via Alternative Self-Dual Teaching
Current weakly supervised semantic segmentation (WSSS) frameworks usually contain the separated mask-refinement model and the main semantic region mining model.
Knowledge Distillation
Weakly supervised Semantic Segmentation
+1
Background-Click Supervision for Temporal Action Localization
Weakly supervised temporal action localization aims at learning the instance-level action pattern from the video-level labels, where a significant challenge is action-context confusion.
Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance
On the other hand, instead of processing the twokinds of data separately, we build a novel dual graph modelto guide the focal stack fusion process using all-focus pat-terns.
Single Image Dehazing with An Independent Detail-Recovery Network
In this paper, we propose a single image dehazing method with an independent Detail Recovery Network (DRN), which considers capturing the details from the input image over a separate network and then integrates them into a coarse dehazed image.
ABMDRNet: Adaptive-Weighted Bi-Directional Modality Difference Reduction Network for RGB-T Semantic Segmentation
Semantic segmentation models gain robustness against poor lighting conditions by virtue of complementary information from visible (RGB) and thermal images.
Ranked #34 on
Thermal Image Segmentation
on MFN Dataset
Strengthen Learning Tolerance for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims at learning to localize objects of interest by only using the image-level labels as the supervision.
A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation
Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN.
Weakly Supervised Object Localization and Detection: A Survey
As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems and has received significant attention in the past decade.
Few-Cost Salient Object Detection with Adversarial-Paced Learning
A fundamental challenge in training the existing deep saliency detection models is the requirement of large amounts of annotated data.
Onfocus Detection: Identifying Individual-Camera Eye Contact from Unconstrained Images
Onfocus detection aims at identifying whether the focus of the individual captured by a camera is on the camera or not.
Densely Nested Top-Down Flows for Salient Object Detection
To this end, this paper revisits the role of top-down modeling in salient object detection and designs a novel densely nested top-down flows (DNTDF)-based framework.
Light Field Saliency Detection With Dual Local Graph Learning and Reciprocative Guidance
In this paper, we model the information fusion within focal stack via graph networks.
Revisiting Anchor Mechanisms for Temporal Action Localization
To address this problem, this paper proposes a novel anchor-free action localization module that assists action localization by temporal points.
Equivalent Classification Mapping for Weakly Supervised Temporal Action Localization
The existing methods can be categorized into two localization-by-classification pipelines, i. e., the pre-classification pipeline and the post-classification pipeline.
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter.
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 #7 on
Co-Salient Object Detection
on CoCA
Reinforcement Cutting-Agent Learning for Video Object Segmentation
In this paper, we formulate this problem as a Markov Decision Process, where agents are learned to segment object regions under a deep reinforcement learning framework.
PoseFlow: A Deep Motion Representation for Understanding Human Behaviors in Videos
This "noisy" motion representation makes it very challenging for pose estimation and action recognition in real scenarios.
Dilated Temporal Relational Adversarial Network for Generic Video Summarization
Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner.
Unsupervised Object-Level Video Summarization with Online Motion Auto-Encoder
Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos every day, and the underlying fine-grained semantic and motion information (i. e., objects of interest and their key motions) in online videos has been barely touched.
Supervision by Fusion: Towards Unsupervised Learning of Deep Salient Object Detector
Based on this insight, we combine an intra-image fusion stream and a inter-image fusion stream in the proposed framework to generate the learning curriculum and pseudo ground-truth for supervising the training of the deep salient object detector.
Learning Category-Specific 3D Shape Models From Weakly Labeled 2D Images
Recently, researchers have made great processes to build category-specific 3D shape models from 2D images with manual annotations consisting of class labels, keypoints, and ground truth figure-ground segmentations.
SPFTN: A Self-Paced Fine-Tuning Network for Segmenting Objects in Weakly Labelled Videos
Object segmentation in weakly labelled videos is an interesting yet challenging task, which aims at learning to perform category-specific video object segmentation by only using video-level tags.
Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-paced Curriculum Learning
Weakly-supervised object detection (WOD) is a challenging problems in computer vision.
Ranked #40 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
Object Co-Segmentation via Graph Optimized-Flexible Manifold Ranking
Aiming at automatically discovering the common objects contained in a set of relevant images and segmenting them as foreground simultaneously, object co-segmentation has become an active research topic in recent years.
A Review of Co-saliency Detection Technique: Fundamentals, Applications, and Challenges
Co-saliency detection is a newly emerging and rapidly growing research area in computer vision community.
A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection
As an interesting and emerging topic, co-saliency detection aims at simultaneously extracting common salient objects in a group of images.
Co-Saliency Detection via Looking Deep and Wide
In the proposed framework, the wide and deep information are explored for the object proposal windows extracted in each image, and the co-saliency scores are calculated by integrating the intra-image contrast and intra group consistency via a principled Bayesian formulation.
Predicting Eye Fixations Using Convolutional Neural Networks
It is believed that eye movements in free-viewing of natural scenes are directed by both bottom-up visual saliency and top-down visual factors.
