Papers Object Detection
“Object Detection” 태그가 달린 논문 12,160편 · 필터 해제
ByteTraX: Enhancing the ByteTrack Architecture with Optimised Thresholding
The ByteTrack algorithm is a widely used and computationally efficient multi-object tracking architecture. Its core innovation lies in the combination of lenient bounding box associations with tracklet similarity matchin…
Multi-Object TrackingObject DetectionTracking the Unseen: An Occlusion-Robust Framework for Target Tracking Under Full and Long-Term Occlusion
Real-time multi-object tracking systems remain highly vulnerable to full and long-term occlusion, where targets temporarily or completely disappear from the camera's field of view. Conventional trackers may terminate tra…
Multiple Object TrackingMulti-Object TrackingObject DetectionChannel-Wise and Token-Aware Post-Training Quantization for Visual State Space Duality
State space models (SSMs), particularly Mamba, have emerged as efficient alternatives to attention-based architectures and have been extended to vision through ViM, VMamba, and Visual State Space Duality (VSSD). Yet the …
Semantic SegmentationInstance SegmentationObject DetectionTAPe+ML: A Compact Structured Representation for Multi-Task Computer Vision
We present TAPe+ML v3, a compact computer vision system based on TAPe (Theory of Active Perception), a structured representation that encodes relations among perceptual elements before recognition. Instead of operating d…
Instance SegmentationImage ClassificationObject LocalizationObject DetectionFeature Recovery for Object Understanding After Irreversible Fire Damage
Objects in post-fire environments often undergo irreversible physical transformations that change their geometry, material state, and visual appearance. Detecting and identifying these remnants is critical for locating h…
Object DetectionVague2Detect: Handling Ambiguous Prompts in Knowledge-Based Open-World Detection
Real-world detectors must often interpret functional or ambiguous prompts, yet conventional models such as YOLO remain restricted to fixed class lists. Even open-vocabulary models like YOLO-World frequently misalign vagu…
Object DetectionHyperbolic Geometry for Open-World Object Detection in Remote Sensing Imagery
Open-world object detection (OWOD) extends closed-set detection by requiring models to identify unknown objects and incrementally learn them once annotations become available. In remote sensing imagery, object categories…
Incremental LearningObject DetectionMetric LearningScalable Detection of Fossil Palynomorphs in Multifocal Digital Microscopy Images
Palynomorphs (microscopic, organic-walled fossils such as pollen, spores, and dinoflagellates) are important high-resolution records of past climates and are critical to the study of ancient ecosystems. Existing methods …
Object DetectionEfficient Multi-Timescale Event Representations for Feed-Forward Object Detection
Autonomous systems require robust low-latency perception under rapidly changing scene dynamics and challenging illumination. In event cameras object detection commonly relies on recurrent architectures to accumulate spar…
Object DetectionReal-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection
High-dynamic-range (HDR) images, with their rich tone and detail reproduction, hold significant potential to enhance computer vision systems, particularly in autonomous driving. However, most neural networks for embedded…
Autonomous DrivingObject DetectionSeeing the Unseen: Camouflaged Object Detection Beyond the Visible Spectrum
Recent advances in camouflaged object detection (COD) have led to substantial progress in challenging low-visibility scenarios, with pioneering studies demonstrating notable success in localizing objects in camouflaged s…
Object DetectionA Lightweight Phenology-Aware YOLOv5 Framework for Tomato Growth Stage Detection in Resource-Constrained Bhutanese Greenhouse Environments
Accurate detection of tomato growth stages is essential for stage-specific greenhouse management and precision agriculture. In Bhutan, greenhouse cultivation is affected by altitude variability, large diurnal temperature…
Object DetectionLossy Event Compression: From Event Stream Distortion to Task Performance
Event cameras generate asynchronous, sparse data streams with microsecond temporal resolution, but in moderate-to-high motion scenes they can produce as many as hundreds of millions of events per second, creating signifi…
Optical Flow EstimationVideo ReconstructionObject DetectionWALDO: One-Shot Exemplar-Conditioned Object Detection in Cluttered Scenes
Locating a specific object instance in a cluttered scene using a single reference image and a short description, and reporting when that instance is absent, large vision-language models usually address this task. We ask …
Object LocalizationObject DetectionuScenes: A Multimodal RGB and 3D Sonar Dataset for Underwater Robot Perception
Robust perception is essential for the deployment of autonomous underwater robots. However, optical cameras become unreliable under poor illumination and backscatter. Forward looking (2D) acoustic sensors remain effectiv…
Representation LearningScene UnderstandingObject DetectionPoint CloudsSocialized Detector Learning: Trajectory-Guided and Reciprocal Distillation for Heterogeneous Object Detectors
Object detection knowledge is fragmented across independently trained, heterogeneous detectors with complementary category supports. In socialized learning, this knowledge resides in a society, and learning aims to evolv…
Object DetectionRSFusionDet: Underwater RGB-Sonar Multimodal Object Detection
Underwater unimodal object detection faces many challenges in sensor imaging, such as optical images limited by underwater noise and visible distance, and sonar images limited by less object structural information. While…
Object DetectionOn the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift
Accurate agricultural weed detection in real-world field conditions is essential for precision agriculture, enabling targeted intervention and reducing yield loss. Recent work has reported strong detection performance fr…
Object DetectionIs Visual Prompting All You Need? Studying VLM Spatial Reasoning under Progressive Visual Scaffolds
Vision-language models (VLMs) have advanced rapidly in multimodal reasoning, yet recent work shows that their failures often reflect an interaction between visual grounding and downstream reasoning. What remains less cle…
Multimodal ReasoningSpatial ReasoningVisual GroundingObject DetectionA2DINOv3: Rethinking Multi-Modal Object Detection via Socialized Collaboration
Multi-modal object detection is essential for robust scene understanding in challenging conditions, including low-light and adverse environments. Recent vision foundation models (e.g., DINOv3) have exhibited strong repre…
Representation LearningScene UnderstandingAutonomous DrivingObject Detection