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Found 45 papers, 37 papers with code
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler
In this paper, we observe that an adaptive domain scheduler benefits more in OSDG compared with prefixed sequential and random domain schedulers.
Probing Fine-Grained Action Understanding and Cross-View Generalization of Foundation Models
This is the first systematic study of different foundation models and specific design choices for human activity recognition from unknown views, conducted with the goal to provide guidance for backbone- and temporal- fusion scheme selection.
Occlusion-Aware Seamless Segmentation
Our method achieves state-of-the-art performance on the BlendPASS dataset, reaching a remarkable mAPQ of 26. 58% and mIoU of 43. 66%.
Open Panoramic Segmentation
To tackle this problem, in this work, we define a new task termed Open Panoramic Segmentation (OPS), where models are trained with FoV-restricted pinhole images in the source domain in an open-vocabulary setting while evaluated with FoV-open panoramic images in the target domain, enabling the zero-shot open panoramic semantic segmentation ability of models.
Referring Atomic Video Action Recognition
Since these existing methods underperform on RAVAR, we introduce RefAtomNet -- a novel cross-stream attention-driven method specialized for the unique challenges of RAVAR: the need to interpret a textual referring expression for the targeted individual, utilize this reference to guide the spatial localization and harvest the prediction of the atomic actions for the referring person.
Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?
The current state of machine learning scholarship in Timeseries Anomaly Detection (TAD) is plagued by the persistent use of flawed evaluation metrics, inconsistent benchmarking practices, and a lack of proper justification for the choices made in novel deep learning-based model designs.
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.
EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous Driving
This paper introduces the task of Auditory Referring Multi-Object Tracking (AR-MOT), which dynamically tracks specific objects in a video sequence based on audio expressions and appears as a challenging problem in autonomous driving.
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.
LF Tracy: A Unified Single-Pipeline Approach for Salient Object Detection in Light Field Cameras
Research on inter-network data connectivity is scant.
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.
Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained Environments
The framework enhances 3D MobileNet, a neural architecture optimized for speed in video classification, by incorporating knowledge distillation and model quantization to balance model accuracy and computational efficiency.
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.
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.
Towards Privacy-Supporting Fall Detection via Deep Unsupervised RGB2Depth Adaptation
In this paper, we introduce a privacy-supporting solution that makes the RGB-trained model applicable in depth domain and utilizes depth data at test time for fall detection.
OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation
To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective Sub-Aperture Fusion Module (SAFM).
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.
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.
Exploring Few-Shot Adaptation for Activity Recognition on Diverse Domains
In this work, we focus on Few-Shot Domain Adaptation for Activity Recognition (FSDA-AR), which leverages a very small amount of labeled target videos to achieve effective adaptation.
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.
Delivering Arbitrary-Modal Semantic Segmentation
To make this possible, we present the arbitrary cross-modal segmentation model CMNeXt.
Ranked #1 on
Semantic Segmentation
on Porto
Towards Activated Muscle Group Estimation in the Wild
To this intent, we provide the MuscleMap dataset featuring >15K video clips with 135 different activities and 20 labeled muscle groups.
MateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments
People with Visual Impairments (PVI) typically recognize objects through haptic perception.
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{\deg} imagery.
Ranked #1 on
Semantic Segmentation
on SynPASS
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.
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.
Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates
In this work, we for the first time examine how well the confidence values of modern driver observation models indeed match the probability of the correct outcome and show that raw neural network-based approaches tend to significantly overestimate their prediction quality.
A Comparative Analysis of Decision-Level Fusion for Multimodal Driver Behaviour Understanding
Visual recognition inside the vehicle cabin leads to safer driving and more intuitive human-vehicle interaction but such systems face substantial obstacles as they need to capture different granularities of driver behaviour while dealing with highly limited body visibility and changing illumination.
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.
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
Furthermore, we introduce two optimization modules to enhance the patch embedding distillation from different perspectives: (1) Global-Local Context Mixer (GL-Mixer) extracts both global and local information of a representative embedding; (2) Embedding Assistant (EA) acts as an embedding method to seamlessly bridge teacher and student models with the teacher's number of channels.
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.
Affect-DML: Context-Aware One-Shot Recognition of Human Affect using Deep Metric Learning
Human affect recognition is a well-established research area with numerous applications, e. g., in psychological care, but existing methods assume that all emotions-of-interest are given a priori as annotated training examples.
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)
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 #18 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.
PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data
The recent publication of the SemanticKITTI dataset stimulates the research on semantic segmentation of LiDAR point clouds in urban scenarios.
