Papers Video Object Detection
“Video Object Detection” 태그가 달린 논문 159편 · 필터 해제
Dual-Correlation Hypergraph Network for Unaligned RGBT Video Object Detection and A Large-scale Benchmark
RGB-Thermal (RGBT) Video Object Detection (VOD) has gained significant traction due to its ability to overcome the limitations of conventional RGB-based VOD under challenging conditions. However, spatial misalignment com…
Video Object DetectionLearn Temporal Consistency For Robust Satellite Video Detector
Satellite video object detection (SVOD) for oriented and fine-grained objects plays an important role in satellite applications. Most existing SVOD methods only focus on one or a few coarse-grained categories of moving o…
Representation LearningVideo Object DetectionMR2-ByteTrack: CNN and Transformer-based Video Object Detection for AI-augmented Embedded Vision Sensor Nodes
Modern smart vision sensors need on-device intelligence to process video streams, as cloud computing is often impractical due to bandwidth, latency, and privacy constraints. However, these sensory systems typically rely …
Video Object DetectionVVitCutLER: Towards Unsupervised Object Detection and Segmentation in Videos
Unsupervised pixel-level video understanding remains challenging in real-world scenarios, where motion blur, occlusion, and fast object dynamics often cause temporal drift and flickering pseudo-labels.We propose VVitCutL…
Video Object DetectionInstance SegmentationGAP-MLLM: Geometry-Aligned Pre-training for Activating 3D Spatial Perception in Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) demonstrate exceptional semantic reasoning but struggle with 3D spatial perception when restricted to pure RGB inputs. Despite leveraging implicit geometric priors from 3D reconst…
Video Object Detection3D dense captioning3D ReconstructionVisual GroundingDetection of Autonomous Shuttles in Urban Traffic Images Using Adaptive Residual Context
The progressive automation of transport promises to enhance safety and sustainability through shared mobility. Like other vehicles and road users, and even more so for such a new technology, it requires monitoring to und…
Video Object DetectionScene UnderstandingTemporal Object-Aware Vision Transformer for Few-Shot Video Object Detection
Few-shot Video Object Detection (FSVOD) addresses the challenge of detecting novel objects in videos with limited labeled examples, overcoming the constraints of traditional detection methods that require extensive train…
Video Object DetectionNovisVQ: A Streaming Convolutional Neural Network for No-Reference Opinion-Unaware Frame Quality Assessment
Video quality assessment (VQA) is vital for computer vision tasks, but existing approaches face major limitations: full-reference (FR) metrics require clean reference videos, and most no-reference (NR) models depend on t…
Video Quality AssessmentImage Quality AssessmentVideo Object DetectionCan You Trust What You See? Alpha Channel No-Box Attacks on Video Object Detection
As object detection models are increasingly deployed in cyber-physical systems such as autonomous vehicles (AVs) and surveillance platforms, ensuring their security against adversarial threats is essential. While prior w…
Video Object DetectionAutonomous VehiclesAdversarial AttackStructured Universal Adversarial Attacks on Object Detection for Video Sequences
Video-based object detection plays a vital role in safety-critical applications. While deep learning-based object detectors have achieved impressive performance, they remain vulnerable to adversarial attacks, particularl…
Video Object DetectionAdversarial AttackMVP: Motion Vector Propagation for Zero-Shot Video Object Detection
Running a large open-vocabulary (Open-vocab) detector on every video frame is accurate but expensive. We introduce a training-free pipeline that invokes OWLv2 only on fixed-interval keyframes and propagates detections to…
Video Object DetectionContrastive Learning through Auxiliary Branch for Video Object Detection
Video object detection is a challenging task because videos often suffer from image deterioration such as motion blur, occlusion, and deformable shapes, making it significantly more difficult than detecting objects in st…
Video Object DetectionContrastive LearningLightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos
Modern image-based object detection models, such as YOLOv7, primarily process individual frames independently, thus ignoring valuable temporal context naturally present in videos. Meanwhile, existing video-based detectio…
Autonomous DrivingComputational Efficiencyobject-detectionObject Detection+1Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection
RGB-Thermal Video Object Detection (RGBT VOD) can address the limitation of traditional RGB-based VOD in challenging lighting conditions, making it more practical and effective in many applications. However, similar to m…
Graph LearningGraph Representation Learningobject-detectionObject Detection+2Context in object detection: a systematic literature review
Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and e…
Few-Shot Object DetectionObjectobject-detectionObject Detection+4Region Masking to Accelerate Video Processing on Neuromorphic Hardware
The rapidly growing demand for on-chip edge intelligence on resource-constrained devices has motivated approaches to reduce energy and latency of deep learning models. Spiking neural networks (SNNs) have gained particula…
object-detectionObject DetectionVideo Object DetectionTGBFormer: Transformer-GraphFormer Blender Network for Video Object Detection
Video object detection has made significant progress in recent years thanks to convolutional neural networks (CNNs) and vision transformers (ViTs). Typically, CNNs excel at capturing local features but struggle to model …
GPUobject-detectionObject DetectionVideo Object DetectionBeyond Boxes: Mask-Guided Spatio-Temporal Feature Aggregation for Video Object Detection
The primary challenge in Video Object Detection (VOD) is effectively exploiting temporal information to enhance object representations. Traditional strategies, such as aggregating region proposals, often suffer from feat…
GPUMulti-Object TrackingObjectobject-detection+4Estimation of Psychosocial Work Environment Exposures Through Video Object Detection. Proof of Concept Using CCTV Footage
This paper examines the use of computer vision algorithms to estimate aspects of the psychosocial work environment using CCTV footage. We present a proof of concept for a methodology that detects and tracks people in vid…
object-detectionObject DetectionPose EstimationVideo Object DetectionMixture of Scale Experts for Alignment-free RGBT Video Object Detection and A Unified Benchmark
Existing RGB-Thermal Video Object Detection (RGBT VOD) methods predominantly rely on the manual alignment of image pairs, that is both labor-intensive and time-consuming. This dependency significantly restricts the scala…
object-detectionObject DetectionVideo Object Detection