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Found 10 papers, 4 papers with code
PolyMaX: General Dense Prediction with Mask Transformer
Despite this shift, methods based on the per-pixel prediction paradigm still dominate the benchmarks on the other dense prediction tasks that require continuous outputs, such as depth estimation and surface normal prediction.
Ranked #2 on
Surface Normals Estimation
on NYU Depth v2
SANPO: A Scene Understanding, Accessibility, Navigation, Pathfinding, Obstacle Avoidance Dataset
All synthetic sessions and a subset of real sessions have temporally consistent dense panoptic segmentation labels.
Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset
We thoroughly benchmark audiovisual classification performance and modality fusion experiments through the use of state-of-the-art transformer methods.
On Label Granularity and Object Localization
Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels.
Bridging the Gap Between Object Detection and User Intent via Query-Modulation
When interacting with objects through cameras, or pictures, users often have a specific intent.
When Does Contrastive Visual Representation Learning Work?
Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification.
Benchmarking Representation Learning for Natural World Image Collections
In order to facilitate progress in this area we present two new natural world visual classification datasets, iNat2021 and NeWT.
On the Reproducibility of Neural Network Predictions
By analyzing the relationship between churn and prediction confidences, we pursue an approach with two components for churn reduction.
Improving Calibration in Deep Metric Learning With Cross-Example Softmax
Triplet-based methods capture top-$k$ relevancy, where all top-$k$ scoring documents are assumed to be relevant to a given query Pairwise contrastive models capture threshold relevancy, where all documents scoring higher than some threshold are assumed to be relevant.
Understanding Image Quality and Trust in Peer-to-Peer Marketplaces
In this work, we conducted a large-scale study on the quality of user-generated images in peer-to-peer marketplaces.
