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Found 65 papers, 47 papers with code
Segment Any Medical Model Extended
To this end, a unified platform helps push the boundary of the foundation model for medical images, facilitating the use, modification, and validation of SAM and its variants in medical image segmentation.
Realtime Robust Shape Estimation of Deformable Linear Object
We propose a robust method to estimate the shape of linear deformable objects in realtime using scattered and unordered key points.
RoDLA: Benchmarking the Robustness of Document Layout Analysis Models
To address this, we are the first to introduce a robustness benchmark for DLA models, which includes 450K document images of three datasets.
Skeleton-Based Human Action Recognition with Noisy Labels
In this study, we bridge this gap by implementing a framework that augments well-established skeleton-based human action recognition methods with label-denoising strategies from various research areas to serve as the initial benchmark.
OccFiner: Offboard Occupancy Refinement with Hybrid Propagation
Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision.
Large Language Model for Table Processing: A Survey
Tables, typically two-dimensional and structured to store large amounts of data, are essential in daily activities like database queries, spreadsheet calculations, and generating reports from web tables.
LF Tracy: A Unified Single-Pipeline Approach for Salient Object Detection in Light Field Cameras
Previous approaches predominantly employ a custom two-stream design to discover the implicit angular feature within light field cameras, leading to significant information isolation between different LF representations.
Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation
Integrating information from multiple modalities enhances the robustness of scene perception systems in autonomous vehicles, providing a more comprehensive and reliable sensory framework.
Navigating Open Set Scenarios for Skeleton-based Action Recognition
In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones.
Adversarial Prompt Tuning for Vision-Language Models
With the rapid advancement of multimodal learning, pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capacities in bridging the gap between visual and language modalities.
CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity
Roadside camera-driven 3D object detection is a crucial task in intelligent transportation systems, which extends the perception range beyond the limitations of vision-centric vehicles and enhances road safety.
Ranked #2 on
3D Object Detection
on Rope3D
Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision
These works overlooked the differences in performance among modalities, which led to the propagation of erroneous knowledge between modalities while only three fundamental modalities, i. e., joints, bones, and motions are used, hence no additional modalities are explored.
Unveiling the Hidden Realm: Self-supervised Skeleton-based Action Recognition in Occluded Environments
To integrate action recognition methods into autonomous robotic systems, it is crucial to consider adverse situations involving target occlusions.
Joint Modeling of Feature, Correspondence, and a Compressed Memory for Video Object Segmentation
To overcome these issues, we propose a unified VOS framework, coined as JointFormer, for joint modeling the three elements of feature, correspondence, and a compressed memory.
OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation
To avoid feature loss during network propagation and simultaneously streamline the redundant information from the light field camera, we present a simple yet very effective Sub-Aperture Fusion Module (SAFM) to embed sub-aperture images into angular features without any additional memory cost.
Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents
On the other hand, the entire line segment detected by the visual subsystem overcomes the limitation of the LiDAR subsystem, which can only perform the local calculation for geometric features.
Open Scene Understanding: Grounded Situation Recognition Meets Segment Anything for Helping People with Visual Impairments
Grounded Situation Recognition (GSR) is capable of recognizing and interpreting visual scenes in a contextually intuitive way, yielding salient activities (verbs) and the involved entities (roles) depicted in images.
Introducing Foundation Models as Surrogate Models: Advancing Towards More Practical Adversarial Attacks
Harnessing the concept of non-robust features, we elaborate on two guiding principles for surrogate model selection to explain why the foundational model is an optimal choice for this role.
Federated Unlearning via Active Forgetting
To this end, we propose a novel federated unlearning framework based on incremental learning, which is independent of specific models and federated settings.
Line Graphics Digitization: A Step Towards Full Automation
The digitization of documents allows for wider accessibility and reproducibility.
RelaMiX: Exploring Few-Shot Adaptation in Video-based Action Recognition
Domain adaptation is essential for activity recognition to ensure accurate and robust performance across diverse environments, sensor types, and data sources.
Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving
At the same time, an Across-Space Loss (ASL) is designed to mitigate the negative impact of geometric distortions.
SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM
To assist with the development, assessment, and application of SAM on medical images, we introduce Segment Any Medical Model (SAMM), an extension of SAM on 3D Slicer - an image processing and visualization software extensively used by the medical imaging community.
FishDreamer: Towards Fisheye Semantic Completion via Unified Image Outpainting and Segmentation
This paper raises the new task of Fisheye Semantic Completion (FSC), where dense texture, structure, and semantics of a fisheye image are inferred even beyond the sensor field-of-view (FoV).
360BEV: Panoramic Semantic Mapping for Indoor Bird's-Eye View
Seeing only a tiny part of the whole is not knowing the full circumstance.
MuscleMap: Towards Video-based Activated Muscle Group Estimation in the Wild
In this paper, we tackle the new task of video-based Activated Muscle Group Estimation (AMGE) aiming at identifying active muscle regions during physical activity in the wild.
Delivering Arbitrary-Modal Semantic Segmentation
To make this possible, we present the arbitrary cross-modal segmentation model CMNeXt.
Ranked #1 on
Semantic Segmentation
on DSEC
MateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments
People with Visual Impairments (PVI) typically recognize objects through haptic perception.
Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples
Furthermore, we propose to leverage VisionandLanguage Pre-trained Models (VLPMs) like CLIP as the surrogate model to improve the transferability of the crafted UCs to diverse domains.
Computational Imaging for Machine Perception: Transferring Semantic Segmentation beyond Aberrations
Further, we propose Computational Imaging Assisted Domain Adaptation (CIADA) to leverage prior knowledge of CI for robust performance in SSOA.
Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation
In this paper, we address panoramic semantic segmentation which is under-explored due to two critical challenges: (1) image distortions and object deformations on panoramas; (2) lack of semantic annotations in the 360-degree imagery.
Ranked #1 on
Semantic Segmentation
on SynPASS
Multi-modal Depression Estimation based on Sub-attentional Fusion
Failure to timely diagnose and effectively treat depression leads to over 280 million people suffering from this psychological disorder worldwide.
Trans4Map: Revisiting Holistic Bird's-Eye-View Mapping from Egocentric Images to Allocentric Semantics with Vision Transformers
Humans have an innate ability to sense their surroundings, as they can extract the spatial representation from the egocentric perception and form an allocentric semantic map via spatial transformation and memory updating.
Towards Adversarial Attack on Vision-Language Pre-training Models
While vision-language pre-training model (VLP) has shown revolutionary improvements on various vision-language (V+L) tasks, the studies regarding its adversarial robustness remain largely unexplored.
Low-Mid Adversarial Perturbation against Unauthorized Face Recognition System
In light of the growing concerns regarding the unauthorized use of facial recognition systems and its implications on individual privacy, the exploration of adversarial perturbations as a potential countermeasure has gained traction.
Indoor Navigation Assistance for Visually Impaired People via Dynamic SLAM and Panoptic Segmentation with an RGB-D Sensor
Then, poses and speed of tracked dynamic objects can be estimated, which are passed to the users through acoustic feedback.
Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-Learning
Autonomous vehicles utilize urban scene segmentation to understand the real world like a human and react accordingly.
Ranked #1 on
Semantic Segmentation
on DADA-seg
(using extra training data)
MatchFormer: Interleaving Attention in Transformers for Feature Matching
While detector-based methods coupled with feature descriptors struggle in low-texture scenes, CNN-based methods with a sequential extract-to-match pipeline, fail to make use of the matching capacity of the encoder and tend to overburden the decoder for matching.
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
Pixel-wise semantic segmentation of RGB images can be advanced by exploiting complementary features from the supplementary modality (X-modality).
Ranked #1 on
Semantic Segmentation
on SpectralWaste
TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration
This module operates in the latent feature-space enriching and diversifying the training set at feature-level in order to improve generalization to novel data appearances, (e. g., sensor changes) and general feature quality.
Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation
To get around this domain difference and bring together semantic annotations from pinhole- and 360-degree surround-visuals, we propose to learn object deformations and panoramic image distortions in the Deformable Patch Embedding (DPE) and Deformable MLP (DMLP) components which blend into our Transformer for PAnoramic Semantic Segmentation (Trans4PASS) model.
Ranked #2 on
Semantic Segmentation
on SynPASS
TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation
Semantic segmentation benchmarks in the realm of autonomous driving are dominated by large pre-trained transformers, yet their widespread adoption is impeded by substantial computational costs and prolonged training durations.
Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
Yet, the research of data-scarce recognition from skeleton sequences, such as one-shot action recognition, does not explicitly consider occlusions despite their everyday pervasiveness.
Ranked #1 on
Action Classification
on Toyota Smarthome dataset
(Accuracy metric)
Should I take a walk? Estimating Energy Expenditure from Video Data
To study this underresearched task, we introduce Vid2Burn -- an omni-source benchmark for estimating caloric expenditure from video data featuring both, high- and low-intensity activities for which we derive energy expenditure annotations based on models established in medical literature.
Exploring Event-driven Dynamic Context for Accident Scene Segmentation
Moreover, in order to evaluate the segmentation performance in traffic accidents, we provide a pixel-wise annotated accident dataset, namely DADA-seg, which contains a variety of critical scenarios from traffic accidents.
Ranked #3 on
Semantic Segmentation
on DADA-seg
(using extra training data)
Transfer beyond the Field of View: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation
We look at this problem from the perspective of domain adaptation and bring panoramic semantic segmentation to a setting, where labelled training data originates from a different distribution of conventional pinhole camera images.
Ranked #7 on
Semantic Segmentation
on DensePASS
(using extra training data)
Trans4Trans: Efficient Transformer for Transparent Object and Semantic Scene Segmentation in Real-World Navigation Assistance
In this paper, we build a wearable system with a novel dual-head Transformer for Transparency (Trans4Trans) perception model, which can segment general- and transparent objects.
Ranked #2 on
Semantic Segmentation
on DADA-seg
(using extra training data)
Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation
By recognizing the color of pedestrian traffic lights, our prototype can help the user to cross a street safely.
DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange
First, we formalize the task of unsupervised domain adaptation for panoramic semantic segmentation, where a network trained on labelled examples from the source domain of pinhole camera data is deployed in a different target domain of panoramic images, for which no labels are available.
Benign Adversarial Attack: Tricking Models for Goodness
In spite of the successful application in many fields, machine learning models today suffer from notorious problems like vulnerability to adversarial examples.
A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues
Data missingness and quality are common problems in machine learning, especially for high-stakes applications such as healthcare.
Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World
Common fully glazed facades and transparent objects present architectural barriers and impede the mobility of people with low vision or blindness, for instance, a path detected behind a glass door is inaccessible unless it is correctly perceived and reacted.
Ranked #1 on
Semantic Segmentation
on Trans10K
HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor
To tackle these issues, we propose HIDA, a lightweight assistive system based on 3D point cloud instance segmentation with a solid-state LiDAR sensor, for holistic indoor detection and avoidance.
Ranked #17 on
3D Instance Segmentation
on ScanNet(v2)
MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding
At the heart of all automated driving systems is the ability to sense the surroundings, e. g., through semantic segmentation of LiDAR sequences, which experienced a remarkable progress due to the release of large datasets such as SemanticKITTI and nuScenes-LidarSeg.
ImageNet Pre-training also Transfers Non-Robustness
ImageNet pre-training has enabled state-of-the-art results on many tasks.
Pose2Drone: A Skeleton-Pose-based Framework for Human-Drone Interaction
Our framework provides the functionality to control the movement of the drone with simple arm gestures and to follow the user while keeping a safe distance.
Capturing Omni-Range Context for Omnidirectional Segmentation
Convolutional Networks (ConvNets) excel at semantic segmentation and have become a vital component for perception in autonomous driving.
Ranked #10 on
Semantic Segmentation
on DensePASS
(using extra training data)
Perception Framework through Real-Time Semantic Segmentation and Scene Recognition on a Wearable System for the Visually Impaired
As the scene information, including objectness and scene type, are important for people with visual impairment, in this work we present a multi-task efficient perception system for the scene parsing and recognition tasks.
Panoptic Lintention Network: Towards Efficient Navigational Perception for the Visually Impaired
Based on Lintention, we then devise a novel panoptic segmentation model which we term Panoptic Lintention Net.
A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image
To address the flaws in the geometric algorithms, we propose a novel framework based on image-to-image translation to learn a pertinent mapping dependence for synthesizing straightened chromosomes with uninterrupted banding patterns and preserved details.
ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data
Ensuring the safety of all traffic participants is a prerequisite for bringing intelligent vehicles closer to practical applications.
Ranked #6 on
Semantic Segmentation
on KITTI-360
Adversarial Privacy-preserving Filter
While widely adopted in practical applications, face recognition has been critically discussed regarding the malicious use of face images and the potential privacy problems, e. g., deceiving payment system and causing personal sabotage.
Can we cover navigational perception needs of the visually impaired by panoptic segmentation?
Navigational perception for visually impaired people has been substantially promoted by both classic and deep learning based segmentation methods.
blessing in disguise: Designing Robust Turing Test by Employing Algorithm Unrobustness
Turing test was originally proposed to examine whether machine's behavior is indistinguishable from a human.
Attention, Please! Adversarial Defense via Activation Rectification and Preservation
This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change.
