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Found 5 papers, 1 papers with code
Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity.
I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation
This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation.
Scene-Intuitive Agent for Remote Embodied Visual Grounding
Intuitively, we comprehend the semantics of the instruction to form an overview of where a bathroom is and what a blue towel is in mind; then, we navigate to the target location by consistently matching the bathroom appearance in mind with the current scene.
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization
Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works.
Ranked #22 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up
We build complementary parts models in a weakly supervised manner to retrieve information suppressed by dominant object parts detected by convolutional neural networks.
Ranked #20 on
Fine-Grained Image Classification
on CUB-200-2011
