Search Results for author:
Found 13 papers, 6 papers with code
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss
This paper proposes a hierarchical loss for monocular depth estimation, which measures the differences between the prediction and ground truth in hierarchical embedding spaces of depth maps.
Deep Boosting Learning: A Brand-new Cooperative Approach for Image-Text Matching
Specifically, we propose a brand-new Deep Boosting Learning (DBL) algorithm, where an anchor branch is first trained to provide insights into the data properties, with a target branch gaining more advanced knowledge to develop optimal features and distance metrics.
Plug-and-Play Regulators for Image-Text Matching
Exploiting fine-grained correspondence and visual-semantic alignments has shown great potential in image-text matching.
Ranked #2 on
Image Retrieval
on Flickr30K 1K test
Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline
With the popularity of multi-modal sensors, visible-thermal (RGB-T) object tracking is to achieve robust performance and wider application scenarios with the guidance of objects' temperature information.
Ranked #8 on
Rgb-T Tracking
on GTOT
High-Performance Transformer Tracking
First, we present a transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head.
Self-Supervised Pretraining for RGB-D Salient Object Detection
Existing CNNs-Based RGB-D salient object detection (SOD) networks are all required to be pretrained on the ImageNet to learn the hierarchy features which helps provide a good initialization.
Detect Globally, Refine Locally: A Novel Approach to Saliency Detection
Moreover, to effectively recover object boundaries, we propose a local Boundary Refinement Network (BRN) to adaptively learn the local contextual information for each spatial position.
Ranked #15 on
RGB Salient Object Detection
on DUTS-TE
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
In addition, to achieve accurate boundary inference and semantic enhancement, edge-aware feature maps in low-level layers and the predicted results of low resolution features are recursively embedded into the learning framework.
Ranked #22 on
RGB Salient Object Detection
on DUTS-TE
(max F-measure metric)
Learning to Detect Salient Objects With Image-Level Supervision
In the second stage, FIN is fine-tuned with its predicted saliency maps as ground truth.
Sample-Specific SVM Learning for Person Re-Identification
Person re-identification addresses the problem of matching people across disjoint camera views and extensive efforts have been made to seek either the robust feature representation or the discriminative matching metrics.
Salient Object Detection via Bootstrap Learning
Furthermore, we show that the proposed bootstrap learning approach can be easily applied to other bottom-up saliency models for significant improvement.
Deep Networks for Saliency Detection via Local Estimation and Global Search
In the global search stage, the local saliency map together with global contrast and geometric information are used as global features to describe a set of object candidate regions.
Saliency Detection via Graph-Based Manifold Ranking
The saliency of the image elements is defined based on their relevances to the given seeds or queries.
