Ming-Hsuan Yang is a professor in Electrical Engineering and Computer Science at University of California, Merced and a research scientist at Google. Supervisors: Narendra Ahuja and Dan Roth
The study of mouse social behaviours has been increasingly undertaken in neuroscience research. H... more The study of mouse social behaviours has been increasingly undertaken in neuroscience research. However, automated quantification of mouse behaviours from the videos of interacting mice is still a challenging problem, where object tracking plays a key role in locating mice in their living spaces. Artificial markers are often applied for multiple mice tracking, which are intrusive and consequently interfere with the movements of mice in a dynamic environment. In this paper, we propose a novel method to continuously track several mice and individual parts without requiring any specific tagging. Firstly, we propose an efficient and robust deep learning based mouse part detection scheme to generate part candidates. Subsequently, we propose a novel Bayesian Integer Linear Programming Model that jointly assigns the part candidates to individual targets with necessary geometric constraints whilst establishing pair-wise association between the detected parts. There is no publicly available ...
The recent years have witnessed advances in parallel algorithms for large scale optimization prob... more The recent years have witnessed advances in parallel algorithms for large scale optimization problems. Notwithstanding demonstrated success, existing algorithms that parallelize over features are usually limited by divergence issues under high parallelism or require data preprocessing to alleviate these problems. In this work, we propose a Parallel Coordinate Descent Newton algorithm using multidimensional approximate Newton steps (PCDN), where the off-diagonal elements of the Hessian are set to zero to enable parallelization. It randomly partitions the feature set into b bundles/subsets with size of P, and sequentially processes each bundle by first computing the descent directions for each feature in parallel and then conducting P-dimensional line search to obtain the step size. We show that: (1) PCDN is guaranteed to converge globally despite increasing parallelism; (2) PCDN converges to the specified accuracy within the limited iteration number of T, and T decreases with increas...
In this work, we propose a simple yet effective meta-learning algorithm in thesemi-supervised set... more In this work, we propose a simple yet effective meta-learning algorithm in thesemi-supervised settings. We notice that existing consistency-based approachesmostly do not consider the essential role of the label information for consistencyregularization. To alleviate this issue, we bridge the relationship between theconsistency loss and label information by unfolding and differentiating throughone optimization step. Specifically, we exploit the pseudo labels of the unlabeledexamples which are guided by the meta-gradients of the labeled data loss so thatthe model can generalize well on the labeled examples. In addition, we introduce asimple first-order approximation to avoid computing higher-order derivatives andguarantee scalability. Extensive evaluations on the SVHN, CIFAR, and ImageNetdatasets demonstrate that the proposed algorithm performs favorably against thestate-of-the-art methods.
Obtaining object response maps is one important step to achieve weakly-supervised semantic segmen... more Obtaining object response maps is one important step to achieve weakly-supervised semantic segmentation using image-level labels. However, existing methods rely on the classification task, which could result in a response map only attending on discriminative object regions as the network does not need to see the entire object for optimizing the classification loss. To tackle this issue, we propose a principled and end-to-end train-able framework to allow the network to pay attention to other parts of the object, while producing a more complete and uniform response map. Specifically, we introduce the mixup data augmentation scheme into the classification network and design two uncertainty regularization terms to better interact with the mixup strategy. In experiments, we conduct extensive analysis to demonstrate the proposed method and show favorable performance against state-of-the-art approaches.
Quantitative Evaluation. We quantitatively evaluate the proposed long-term correlation tracking (... more Quantitative Evaluation. We quantitatively evaluate the proposed long-term correlation tracking (LCT) algorithm on the 50 benchmark sequences with comparisons to the 11 state-of-the-art trackers, CSK [4], STC [10], KCF [5] MIL [1], Struck [3], CT [11], ASLA [6]), TLD [7], SCM [12], MEEM [9], and TGPR [2]). We report the distance precision at a threshold of 20 pixels in Table 1 and the overlap success rate at a threshold of 0.5 in Table 2. We report the distance precision plots over eight tracking challenges in our attribute-based evaluation in Figure 1 as mentioned on line 643 in the manuscript.
The study of mouse social behaviours has been increasingly undertaken in neuroscience research. H... more The study of mouse social behaviours has been increasingly undertaken in neuroscience research. However, automated quantification of mouse behaviours from the videos of interacting mice is still a challenging problem, where object tracking plays a key role in locating mice in their living spaces. Artificial markers are often applied for multiple mice tracking, which are intrusive and consequently interfere with the movements of mice in a dynamic environment. In this paper, we propose a novel method to continuously track several mice and individual parts without requiring any specific tagging. Firstly, we propose an efficient and robust deep learning based mouse part detection scheme to generate part candidates. Subsequently, we propose a novel Bayesian Integer Linear Programming Model that jointly assigns the part candidates to individual targets with necessary geometric constraints whilst establishing pair-wise association between the detected parts. There is no publicly available ...
In this work, we propose a simple yet effective meta-learning algorithm in thesemi-supervised set... more In this work, we propose a simple yet effective meta-learning algorithm in thesemi-supervised settings. We notice that existing consistency-based approachesmostly do not consider the essential role of the label information for consistencyregularization. To alleviate this issue, we bridge the relationship between theconsistency loss and label information by unfolding and differentiating throughone optimization step. Specifically, we exploit the pseudo labels of the unlabeledexamples which are guided by the meta-gradients of the labeled data loss so thatthe model can generalize well on the labeled examples. In addition, we introduce asimple first-order approximation to avoid computing higher-order derivatives andguarantee scalability. Extensive evaluations on the SVHN, CIFAR, and ImageNetdatasets demonstrate that the proposed algorithm performs favorably against thestate-of-the-art methods.
The study of mouse social behaviours has been increasingly undertaken in neuroscience research. H... more The study of mouse social behaviours has been increasingly undertaken in neuroscience research. However, automated quantification of mouse behaviours from the videos of interacting mice is still a challenging problem, where object tracking plays a key role in locating mice in their living spaces. Artificial markers are often applied for multiple mice tracking, which are intrusive and consequently interfere with the movements of mice in a dynamic environment. In this paper, we propose a novel method to continuously track several mice and individual parts without requiring any specific tagging. Firstly, we propose an efficient and robust deep learning based mouse part detection scheme to generate part candidates. Subsequently, we propose a novel Bayesian Integer Linear Programming Model that jointly assigns the part candidates to individual targets with necessary geometric constraints whilst establishing pair-wise association between the detected parts. There is no publicly available ...
The recent years have witnessed advances in parallel algorithms for large scale optimization prob... more The recent years have witnessed advances in parallel algorithms for large scale optimization problems. Notwithstanding demonstrated success, existing algorithms that parallelize over features are usually limited by divergence issues under high parallelism or require data preprocessing to alleviate these problems. In this work, we propose a Parallel Coordinate Descent Newton algorithm using multidimensional approximate Newton steps (PCDN), where the off-diagonal elements of the Hessian are set to zero to enable parallelization. It randomly partitions the feature set into b bundles/subsets with size of P, and sequentially processes each bundle by first computing the descent directions for each feature in parallel and then conducting P-dimensional line search to obtain the step size. We show that: (1) PCDN is guaranteed to converge globally despite increasing parallelism; (2) PCDN converges to the specified accuracy within the limited iteration number of T, and T decreases with increas...
In this work, we propose a simple yet effective meta-learning algorithm in thesemi-supervised set... more In this work, we propose a simple yet effective meta-learning algorithm in thesemi-supervised settings. We notice that existing consistency-based approachesmostly do not consider the essential role of the label information for consistencyregularization. To alleviate this issue, we bridge the relationship between theconsistency loss and label information by unfolding and differentiating throughone optimization step. Specifically, we exploit the pseudo labels of the unlabeledexamples which are guided by the meta-gradients of the labeled data loss so thatthe model can generalize well on the labeled examples. In addition, we introduce asimple first-order approximation to avoid computing higher-order derivatives andguarantee scalability. Extensive evaluations on the SVHN, CIFAR, and ImageNetdatasets demonstrate that the proposed algorithm performs favorably against thestate-of-the-art methods.
Obtaining object response maps is one important step to achieve weakly-supervised semantic segmen... more Obtaining object response maps is one important step to achieve weakly-supervised semantic segmentation using image-level labels. However, existing methods rely on the classification task, which could result in a response map only attending on discriminative object regions as the network does not need to see the entire object for optimizing the classification loss. To tackle this issue, we propose a principled and end-to-end train-able framework to allow the network to pay attention to other parts of the object, while producing a more complete and uniform response map. Specifically, we introduce the mixup data augmentation scheme into the classification network and design two uncertainty regularization terms to better interact with the mixup strategy. In experiments, we conduct extensive analysis to demonstrate the proposed method and show favorable performance against state-of-the-art approaches.
Quantitative Evaluation. We quantitatively evaluate the proposed long-term correlation tracking (... more Quantitative Evaluation. We quantitatively evaluate the proposed long-term correlation tracking (LCT) algorithm on the 50 benchmark sequences with comparisons to the 11 state-of-the-art trackers, CSK [4], STC [10], KCF [5] MIL [1], Struck [3], CT [11], ASLA [6]), TLD [7], SCM [12], MEEM [9], and TGPR [2]). We report the distance precision at a threshold of 20 pixels in Table 1 and the overlap success rate at a threshold of 0.5 in Table 2. We report the distance precision plots over eight tracking challenges in our attribute-based evaluation in Figure 1 as mentioned on line 643 in the manuscript.
The study of mouse social behaviours has been increasingly undertaken in neuroscience research. H... more The study of mouse social behaviours has been increasingly undertaken in neuroscience research. However, automated quantification of mouse behaviours from the videos of interacting mice is still a challenging problem, where object tracking plays a key role in locating mice in their living spaces. Artificial markers are often applied for multiple mice tracking, which are intrusive and consequently interfere with the movements of mice in a dynamic environment. In this paper, we propose a novel method to continuously track several mice and individual parts without requiring any specific tagging. Firstly, we propose an efficient and robust deep learning based mouse part detection scheme to generate part candidates. Subsequently, we propose a novel Bayesian Integer Linear Programming Model that jointly assigns the part candidates to individual targets with necessary geometric constraints whilst establishing pair-wise association between the detected parts. There is no publicly available ...
In this work, we propose a simple yet effective meta-learning algorithm in thesemi-supervised set... more In this work, we propose a simple yet effective meta-learning algorithm in thesemi-supervised settings. We notice that existing consistency-based approachesmostly do not consider the essential role of the label information for consistencyregularization. To alleviate this issue, we bridge the relationship between theconsistency loss and label information by unfolding and differentiating throughone optimization step. Specifically, we exploit the pseudo labels of the unlabeledexamples which are guided by the meta-gradients of the labeled data loss so thatthe model can generalize well on the labeled examples. In addition, we introduce asimple first-order approximation to avoid computing higher-order derivatives andguarantee scalability. Extensive evaluations on the SVHN, CIFAR, and ImageNetdatasets demonstrate that the proposed algorithm performs favorably against thestate-of-the-art methods.
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Papers by Ming-Hsuan Yang