Pascal VOC
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2016first seen
PASCAL_VOC
Papers using Pascal VOC (57)
- Rethinking Atrous Convolution For Semantic Image SegmentationR-FCN: Object Detection Via Region-based Fully Convolutional NetworksDSSD : Deconvolutional Single Shot DetectorRotate To Attend: Convolutional Triplet Attention ModuleSegnext: Rethinking Convolutional Attention Design For Semantic SegmentationPropagate Yourself: Exploring Pixel-level Consistency For Unsupervised Visual Representation LearningFrustratingly Simple Few-shot Object DetectionWhere Are The Blobs: Counting By Localization With Point SupervisionAutoassign: Differentiable Label Assignment For Dense Object DetectionKnowledge Distillation Via The Target-aware TransformerLight-weight Refinenet For Real-time Semantic SegmentationDense Learning Based Semi-supervised Object DetectionDomain Adaptation for Object Detection via Style ConsistencyDont Even Look Once: Synthesizing Features For Zero-shot DetectionSoft Sampling For Robust Object DetectionRPATTACK: Refined Patch Attack On General Object DetectorsPart-aware Panoptic SegmentationSemantic Segmentation Via Highly Fused Convolutional Network With Multiple Soft Cost FunctionsModeling Missing Annotations For Incremental Learning In Object DetectionSolving Missing-annotation Object Detection With Background Recalibration LossRe-scoring Using Image-language Similarity For Few-shot Object DetectionMulti-class Token Transformer for Weakly Supervised Semantic
SegmentationZero-shot Object Detection By Hybrid Region EmbeddingIn Defense Of Lazy Visual Grounding For Open-vocabulary Semantic SegmentationDeep Object Co-SegmentationSingle-shot Object Detection With Enriched SemanticsInstance Segmentation With Point SupervisionPeekaboo: Text To Image Diffusion Models Are Zero-shot SegmentorsSemi-convolutional Operators for Instance SegmentationDetect Everything With Few ExamplesSpatial Reasoning for Few-Shot Object DetectionSalvage Of Supervision In Weakly Supervised Object DetectionFew-Shot Object Detection with Fully Cross-TransformerYOLO-Former: YOLO Shakes Hand With ViTWeakly- and Semi-Supervised Panoptic SegmentationA Single-shot Object Detector With Feature Aggragation And EnhancementAuto-vocabulary Semantic SegmentationFeature-Driven Super-Resolution for Object DetectionDETReg: Unsupervised Pretraining with Region Priors for Object DetectionTime-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot
Object DetectionImproving Task-specific Representation Via 1M Unlabelled Images Without Any Extra KnowledgeA Comparative Attention Framework For Better Few-shot Object Detection On Aerial ImagesRevisiting DETR Pre-training For Object DetectionEqualization Loss For Large Vocabulary Instance SegmentationIvaNet: Learning to jointly detect and segment objets with the help of
Local Top-Down ModulesSeqCo-DETR: Sequence Consistency Training for Self-Supervised Object
Detection with TransformersDenseDINO: Boosting Dense Self-Supervised Learning with Token-Based
Point-Level ConsistencyCLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic
Segmentation For-FreePractical Insights into Semi-Supervised Object Detection ApproachesExploring Open-Vocabulary Object Recognition in Images using CLIPHUWSOD: Holistic Self-training For Unified Weakly Supervised Object DetectionApproximate Size Targets Are Sufficient For Accurate Semantic SegmentationTowards Rotation Invariance In Object DetectionVEIL: Vetting Extracted Image Labels From In-the-wild Captions For Weakly-supervised Object DetectionSPARCNN: Spatially Related Convolutional Neural NetworksSupervision Interpolation Via Lossmix: Generalizing Mixup For Object Detection And BeyondTowards Few-Annotation Learning for Object Detection: Are
Transformer-based Models More Efficient ?