155 research outputs found
Relaxed Majorization-Minimization for Non-smooth and Non-convex Optimization
We propose a new majorization-minimization (MM) method for non-smooth and
non-convex programs, which is general enough to include the existing MM
methods. Besides the local majorization condition, we only require that the
difference between the directional derivatives of the objective function and
its surrogate function vanishes when the number of iterations approaches
infinity, which is a very weak condition. So our method can use a surrogate
function that directly approximates the non-smooth objective function. In
comparison, all the existing MM methods construct the surrogate function by
approximating the smooth component of the objective function. We apply our
relaxed MM methods to the robust matrix factorization (RMF) problem with
different regularizations, where our locally majorant algorithm shows
advantages over the state-of-the-art approaches for RMF. This is the first
algorithm for RMF ensuring, without extra assumptions, that any limit point of
the iterates is a stationary point.Comment: AAAI1
Human Performance Modeling For Two-Dimensional Dwell-Based Eye Pointing
Recently, Zhang et al. (2010) proposed an effective performance model for dwell-based eye pointing. However, their model was based on a specific circular target condition, without the ability to predict the performance of acquiring conventional rectangular targets. Thus, the applicability of such a model is limited. In this paper, we extend their one-dimensional model to two-dimensional (2D) target conditions. Carrying out two experiments, we have evaluated the abilities of different model candidates to find out the most appropriate one. The new index of difficulty we redefine for 2D eye pointing (IDeye) can properly reflect the asymmetrical impact of target width and height, which the later exceeds the former, and consequently the IDeyemodel can accurately predict the performance for 2D targets. Importantly, we also find that this asymmetry still holds for varying movement directions. According to the results of our study, we provide useful implications and recommendations for gaze-based interactions
TIDE: Temporally Incremental Disparity Estimation via Pattern Flow in Structured Light System
We introduced Temporally Incremental Disparity Estimation Network (TIDE-Net),
a learning-based technique for disparity computation in mono-camera structured
light systems. In our hardware setting, a static pattern is projected onto a
dynamic scene and captured by a monocular camera. Different from most former
disparity estimation methods that operate in a frame-wise manner, our network
acquires disparity maps in a temporally incremental way. Specifically, We
exploit the deformation of projected patterns (named pattern flow ) on captured
image sequences, to model the temporal information. Notably, this newly
proposed pattern flow formulation reflects the disparity changes along the
epipolar line, which is a special form of optical flow. Tailored for pattern
flow, the TIDE-Net, a recurrent architecture, is proposed and implemented. For
each incoming frame, our model fuses correlation volumes (from current frame)
and disparity (from former frame) warped by pattern flow. From fused features,
the final stage of TIDE-Net estimates the residual disparity rather than the
full disparity, as conducted by many previous methods. Interestingly, this
design brings clear empirical advantages in terms of efficiency and
generalization ability. Using only synthetic data for training, our extensitve
evaluation results (w.r.t. both accuracy and efficienty metrics) show superior
performance than several SOTA models on unseen real data. The code is available
on https://github.com/CodePointer/TIDENet
Self-Supervised Deep Visual Odometry with Online Adaptation
Self-supervised VO methods have shown great success in jointly estimating
camera pose and depth from videos. However, like most data-driven methods,
existing VO networks suffer from a notable decrease in performance when
confronted with scenes different from the training data, which makes them
unsuitable for practical applications. In this paper, we propose an online
meta-learning algorithm to enable VO networks to continuously adapt to new
environments in a self-supervised manner. The proposed method utilizes
convolutional long short-term memory (convLSTM) to aggregate rich
spatial-temporal information in the past. The network is able to memorize and
learn from its past experience for better estimation and fast adaptation to the
current frame. When running VO in the open world, in order to deal with the
changing environment, we propose an online feature alignment method by aligning
feature distributions at different time. Our VO network is able to seamlessly
adapt to different environments. Extensive experiments on unseen outdoor
scenes, virtual to real world and outdoor to indoor environments demonstrate
that our method consistently outperforms state-of-the-art self-supervised VO
baselines considerably.Comment: Accepted by CVPR 2020 ora
Online Adaptive Disparity Estimation for Dynamic Scenes in Structured Light Systems
In recent years, deep neural networks have shown remarkable progress in dense
disparity estimation from dynamic scenes in monocular structured light systems.
However, their performance significantly drops when applied in unseen
environments. To address this issue, self-supervised online adaptation has been
proposed as a solution to bridge this performance gap. Unlike traditional
fine-tuning processes, online adaptation performs test-time optimization to
adapt networks to new domains. Therefore, achieving fast convergence during the
adaptation process is critical for attaining satisfactory accuracy. In this
paper, we propose an unsupervised loss function based on long sequential
inputs. It ensures better gradient directions and faster convergence. Our loss
function is designed using a multi-frame pattern flow, which comprises a set of
sparse trajectories of the projected pattern along the sequence. We estimate
the sparse pseudo ground truth with a confidence mask using a filter-based
method, which guides the online adaptation process. Our proposed framework
significantly improves the online adaptation speed and achieves superior
performance on unseen data.Comment: Accpeted by 36th IEEE/RSJ International Conference on Intelligent
Robots and Systems, 202
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