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Found 19 papers, 5 papers with code
DHR: Dual Features-Driven Hierarchical Rebalancing in Inter- and Intra-Class Regions for Weakly-Supervised Semantic Segmentation
Weakly-supervised semantic segmentation (WSS) ensures high-quality segmentation with limited data and excels when employed as input seed masks for large-scale vision models such as Segment Anything.
ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
In this work, we introduce generative model as a data source for synthesizing hard images that benchmark deep models' robustness.
Zero-shot Building Attribute Extraction from Large-Scale Vision and Language Models
The proposed workflow contains two key components: image-level captioning and segment-level captioning for the building images based on the vocabularies pertinent to structural and civil engineering.
MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic Segmentation
Then, we propose a semantic mining module that takes the object masks to refine the pseudo labels in the target domain.
Dual-View Selective Instance Segmentation Network for Unstained Live Adherent Cells in Differential Interference Contrast Images
In this study, we developed a novel deep-learning algorithm called dual-view selective instance segmentation network (DVSISN) for segmenting unstained adherent cells in differential interference contrast (DIC) images.
ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation
Open compound domain adaptation (OCDA) considers the target domain as the compound of multiple unknown homogeneous subdomains.
Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection
In this work, we propose a new domain adaptation framework for semantic segmentation with annotated points via active selection.
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation
Experiments on KITTI, Cityscapes, and Waymo Open Dataset demonstrate the relevance of our approach and show that our method outperforms state-of-the-art algorithms for the tasks of self-supervised monocular depth estimation, object motion segmentation, monocular scene flow estimation, and visual odometry.
Temporal Alignment Prediction for Few-Shot Video Classification
In order to obtain the similarity of a pair of videos, we predict the alignment scores between all pairs of temporal positions in the two videos with the temporal alignment prediction function.
A Transductive Maximum Margin Classifier for Few-Shot Learning
We introduce a transductive maximum margin classifier for few-shot learning (FS-TMMC).
Learning Structures for Deep Neural Networks
We further establish connections between this principle and the theory of Bayesian optimal classification, and empirically verify that larger entropy of the outputs of a deep neural network indeed corresponds to a better classification accuracy.
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives.
Two-phase Pseudo Label Densification for Self-training based Domain Adaptation
However, since only the confident predictions are taken as pseudo labels, existing self-training approaches inevitably produce sparse pseudo labels in practice.
Affinity Fusion Graph-based Framework for Natural Image Segmentation
The proposed framework combines adjacency-graphs and kernel spectral clustering based graphs (KSC-graphs) according to a new definition named affinity nodes of multi-scale superpixels.
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
Finally, to decrease the intra-domain gap, we propose to employ a self-supervised adaptation technique from the easy to the hard split.
Ranked #2 on
Domain Adaptation
on Synscapes-to-Cityscapes
Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
We refer to this process as Leverage.
Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
We take an approach to learn a generalizable embedding space for novel tasks.
PanJoin: A Partition-based Adaptive Stream Join
In this paper, we present a new algorithm called PanJoin which has high throughput on large windows and supports both equi-join and non-equi-join.
Databases
Optimized Cost per Click in Taobao Display Advertising
Moreover, the platform has to be responsible for the business revenue and user experience.
