Search Results for author:
Found 9 papers, 3 papers with code
Unsupervised Object Localization with Representer Point Selection
We propose a novel unsupervised object localization method that allows us to explain the predictions of the model by utilizing self-supervised pre-trained models without additional finetuning.
Zero-shot Referring Image Segmentation with Global-Local Context Features
To overcome this issue, we propose a simple yet effective zero-shot referring image segmentation method by leveraging the pre-trained cross-modal knowledge from CLIP.
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency
We propose a novel probabilistic method employing Bayesian Model Averaging and self-cycle regularization for spatio-temporal correspondence learning in videos within a self-supervised learning framework.
Weakly Supervised Instance Segmentation by Deep Community Learning
We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks.
Image-level Supervised Instance Segmentation
object-detection
+4
Real-Time MDNet
We present a fast and accurate visual tracking algorithm based on the multi-domain convolutional neural network (MDNet).
Superpixel-Based Tracking-By-Segmentation Using Markov Chains
We propose a simple but effective tracking-by-segmentation algorithm using Absorbing Markov Chain (AMC) on superpixel segmentation, where target state is estimated by a combination of bottom-up and top-down approaches, and target segmentation is propagated to subsequent frames in a recursive manner.
Multi-Object Tracking With Quadruplet Convolutional Neural Networks
We propose Quadruplet Convolutional Neural Networks (Quad-CNN) for multi-object tracking, which learn to associate object detections across frames using quadruplet losses.
Tracking-by-Segmentation With Online Gradient Boosting Decision Tree
We evaluate the performance of our tracking algorithm based on the measures for segmentation masks, where our algorithm illustrates superior accuracy compared to the state-of-the-art segmentation-based tracking methods.
Object Localization based on Structural SVM using Privileged Information
We propose a structured prediction algorithm for object localization based on Support Vector Machines (SVMs) using privileged information.
