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Found 6 papers, 5 papers with code
Designing High-Performing Networks for Multi-Scale Computer Vision
Since the emergence of deep learning, the computer vision field has flourished with models improving at a rapid pace on more and more complex tasks.
EffSeg: Efficient Fine-Grained Instance Segmentation using Structure-Preserving Sparsity
In this work, we propose EffSeg performing fine-grained instance segmentation in an efficient way by using our Structure-Preserving Sparsity (SPS) method based on separately storing the active features, the passive features and a dense 2D index map containing the feature indices.
FQDet: Fast-converging Query-based Detector
Recently, two-stage Deformable DETR introduced the query-based two-stage head, a new type of two-stage head different from the region-based two-stage heads of classical detectors as Faster R-CNN.
Category-Level Pose Retrieval with Contrastive Features Learnt with Occlusion Augmentation
In both cases, the idea is to directly predict the pose of an object.
Trident Pyramid Networks: The importance of processing at the feature pyramid level for better object detection
Given their importance, a computer vision network can be divided into three parts: a backbone (generating a feature pyramid), a neck (refining the feature pyramid) and a head (generating the final output).
What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector
We subsequently demonstrate an optimization-based monocular 3D bounding box detector built on top of the self-supervised vehicle orientation estimator without the requirement of expensive labeled data.
