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Found 9 papers, 3 papers with code
OutfitTransformer: Learning Outfit Representations for Fashion Recommendation
For compatibility prediction, we design an outfit token to capture a global outfit representation and train the framework using a classification loss.
SLADE: A Self-Training Framework For Distance Metric Learning
We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data.
Fashion Outfit Complementary Item Retrieval
We evaluate our method on the outfit compatibility, FITB and new retrieval tasks.
A Unified Point-Based Framework for 3D Segmentation
We present a new unified point-based framework for 3D point cloud segmentation that effectively optimizes pixel-level features, geometrical structures and global context priors of an entire scene.
Primitive-based 3D Building Modeling, Sensor Simulation, and Estimation
As we begin to consider modeling large, realistic 3D building scenes, it becomes necessary to consider a more compact representation over the polygonal mesh model.
DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation
In particular, given an image from the source domain and unlabeled samples from the target domain, the generator synthesizes new images on-the-fly to resemble samples from the target domain in appearance and the segmentation network further refines high-level features before predicting semantic maps, both of which leverage feature statistics of sampled images from the target domain.
Drone-based Object Counting by Spatially Regularized Regional Proposal Network
Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e. g., high-level understanding and fine-grained classification).
Ranked #8 on
Object Counting
on CARPK
Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation
Deep learning models such as convolutional neural net- work have been widely used in 3D biomedical segmentation and achieve state-of-the-art performance.
Scalable Object Detection by Filter Compression With Regularized Sparse Coding
Our method can reconstruct filters by minimizing score map error, while sparse coding reconstructs filters by minimizing appearance error.
