Search Results for author:
Found 15 papers, 12 papers with code
Toward Characteristic-Preserving Image-based Virtual Try-On Network
Second, to alleviate boundary artifacts of warped clothes and make the results more realistic, we employ a Try-On Module that learns a composition mask to integrate the warped clothes and the rendered image to ensure smoothness.
Instance-level Human Parsing via Part Grouping Network
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass.
Ranked #6 on
Human Part Segmentation
on CIHP
Fast Video Shot Transition Localization with Deep Structured Models
In order to train a high-performance shot transition detector, we contribute a new database ClipShots, which contains 128636 cut transitions and 38120 gradual transitions from 4039 online videos.
Ranked #3 on
Camera shot boundary detection
on ClipShots
(using extra training data)
Towards Impartial Multi-task Learning
Multi-task learning (MTL) has been widely used in representation learning.
Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching
However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained.
Group Fisher Pruning for Practical Network Compression
Our method can be used to prune any structures including those with coupled channels.
Learning Efficient Detector with Semi-supervised Adaptive Distillation
ADL enlarges the distillation loss for hard-to-learn and hard-to-mimic samples and reduces distillation loss for the dominant easy samples, enabling distillation to work on the single-stage detector first time, even if the student and the teacher are identical.
Data-Driven Neuron Allocation for Scale Aggregation Networks
The most informative output neurons in each block are preserved while others are discarded, and thus neurons for multiple scales are competitively and adaptively allocated.
Ranked #701 on
Image Classification
on ImageNet
Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective
The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points.
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation
For these matters, we propose the following designs to push the performance to new state-of-art: (i) Coefficient of Variation Smoothing to smooth the CAMs adaptively; (ii) Proportional Pseudo-mask Generation to project the expanded CAMs to pseudo-mask based on a new metric indicating the importance of each class on each location, instead of the scores trained from binary classifiers.
Ranked #27 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
So we try to improve WSSS in the aspect of noise mitigation.
Ranked #23 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Slender Object Detection: Diagnoses and Improvements
In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely \textbf{slender objects}.
Ranked #1 on
Object Detection
on COCO+
Learning Segmentation Masks with the Independence Prior
We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.
Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid
To address this issue, we propose a novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, which learns similarities between a query and a gallery cloth by using both global and local representations in multiple scales.
Ranked #4 on
Image Retrieval
on DeepFashion - Consumer-to-shop
(Rank-1 metric)
