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Found 8 papers, 6 papers with code
We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline
While the vast majority of prior work has studied this as a frame-level Image-DAS problem, a few Video-DAS works have sought to additionally leverage the temporal signal present in adjacent frames.
Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation
We create the first benchmark for semi-supervised multi-modal semantic segmentation and also report the robustness to missing modalities.
Ranked #1 on
Semantic Segmentation
on SUN-RGBD
(Mean IoU (test) metric, using extra
training data)
RGBD Semantic Segmentation
Robust Semi-Supervised RGBD Semantic Segmentation
+3
Performance degradation of ImageNet trained models by simple image transformations
ImageNet trained PyTorch models are generally preferred as the off-the-shelf models for direct use or for initialisation in most computer vision tasks.
An Application to Generate Style Guided Compatible Outfit
Fashion recommendation has witnessed a phenomenal growth of research, particularly in the domains of shop-the-look, contextaware outfit creation, personalizing outfit creation etc.
Recommendation of Compatible Outfits Conditioned on Style
Recommendation in the fashion domain has seen a recent surge in research in various areas, for example, shop-the-look, context-aware outfit creation, personalizing outfit creation, etc.
MSN: Efficient Online Mask Selection Network for Video Instance Segmentation
Our method improves the masks from segmentation and propagation branches in an online manner using the Mask Selection Network (MSN) hence limiting the noise accumulation during mask tracking.
Ranked #26 on
Video Instance Segmentation
on YouTube-VIS validation
Audience Creation for Consumables -- Simple and Scalable Precision Merchandising for a Growing Marketplace
Consumable categories, such as grocery and fast-moving consumer goods, are quintessential to the growth of e-commerce marketplaces in developing countries.
SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses
Unsupervised domain adaptive object detection aims to learn a robust detector in the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different.
