In semi-supervised learning (SSL), many approaches follow the effective self-training paradigm with consistency regularization, utilizing threshold heuristics to alleviate label noise. However, such threshold heuristics lead to the underutilization of crucial discriminative information from the excluded data. In this paper, we present OTAMatch, a novel SSL framework that reformulates pseudo-labeling as an optimal transport (OT) assignment problem and simultaneously exploits data with high confidence to mitigate the confirmation bias. Firstly, OTAMatch models the pseudo-label allocation task as a convex minimization problem, facilitating end-to-end optimization with all pseudo-labels and employing the Sinkhorn-Knopp algorithm for efficient approximation. Meanwhile, we incorporate epsilon-greedy posterior regularization and curriculum bias correction strategies to constrain the distribution of OT assignments, improving the robustness with noisy pseudo-labels. Secondly, we propose PseudoNCE, which explicitly exploits pseudo-label consistency with threshold heuristics to maximize mutual information within self-training, significantly boosting the balance of convergence speed and performance. Consequently, our proposed approach achieves competitive performance on various SSL benchmarks. Specifically, OTAMatch substantially outperforms the previous state-of-the-art SSL algorithms in realistic and challenging scenarios, exemplified by a notable 9.45% error rate reduction over SoftMatch on ImageNet with 100K-label split, underlining its robustness and effectiveness.

Finding views with a good composition from an input image is a common but challenging problem. There are usually at least dozens of candidates (regions) in an image, and how to evaluate these candidates is subjective. Most existing methods only use the feature corresponding to each candidate to evaluate the quality. However, the mutual relations between the candidates from an image play an essential role in composing a good shot due to the comparative nature of this problem. Motivated by this, we propose a graph-based module with a gated feature update to model the relations between different candidates. The candidate region features are propagated on a graph that models mutual relations between different regions for mining the useful information such that the relation features and region features are adaptively fused. We design a multi-task loss to train the model, especially, a regularization term is adopted to incorporate the prior knowledge about the relations into the graph. A data augmentation method is also developed by mixing nodes from different graphs to improve the model generalization ability. Experimental results show that the proposed model performs favorably against state-of-the-art methods, and comprehensive ablation studies demonstrate the contribution of each module and graph-based inference of the proposed method.

Image cropping aims at improving the framing of an image by removing its extraneous outer areas, which is widely used in the photography and printing industry. In some cases, the aspect ratio of cropping results is specified depending on some conditions. In this paper, we propose a meta-learning (learning to learn) based aspect ratio specified image cropping method called Mars, which can generate cropping results of different expected aspect ratios. In the proposed method, a base model and two meta-learners are obtained during the training stage. Given an aspect ratio in the test stage, a new model with new parameters can be generated from the base model. Specifically, the two meta-learners predict the parameters of the base model based on the given aspect ratio. The learning process of the proposed method is learning how to learn cropping models for different aspect ratio requirements, which is a typical meta-learning process. In the experiments, the proposed method is evaluated on three datasets and outperforms most state-of-the-art methods in terms of accuracy and speed. In addition, both the intermediate and final results show that the proposed model can predict different cropping windows for an image depending on different aspect ratio requirements.

Image cropping aims at improving the quality of images by removing unwanted outer areas, which is widely used in the photography and printing industry. Most previous cropping methods that don't need bounding box supervision rely on the sliding window mechanism. The sliding window method results in fixed aspect ratios and limits the shape of the cropping region. Moreover, the sliding window method usually produces lots of candidates on the input image, which is very time-consuming. Motivated by these challenges, we formulate image cropping as a sequential decision-making process and propose a reinforcement learning based framework to address this problem, namely Fast Aesthetics-Aware Adversarial Reinforcement Learning (Fast A3RL). Particularly, the proposed method develops an aesthetics-aware reward function, which is dedicated for image cropping. Similar to human's decisionmaking process, we use a comprehensive state representation including both the current observation and historical experience. We train the agent using the actor-critic architecture in an end-to-end manner. The adversarial learning process is also applied during the training stage. The proposed method is evaluated on several popular cropping datasets, in which the images are unseen during training. Experiment results show that our method achieves state-of-the-art performance with much fewer candidate windows and much less time compared with related methods.

Image cropping aims at improving the aesthetic quality of images by adjusting their composition. Most weakly supervised cropping methods (without bounding box supervision) rely on the sliding window mechanism. The sliding window mechanism requires fixed aspect ratios and limits the cropping region with arbitrary size. Moreover, the sliding window method usually produces tens of thousands of windows on the input image which is very time-consuming. Motivated by these challenges, we firstly formulate the aesthetic image cropping as a sequential decision-making process and propose a weakly supervised Aesthetics Aware Reinforcement Learning (A2-RL) framework to address this problem. Particularly, the proposed method develops an aesthetics aware reward function which especially benefits image cropping. Similar to human's decision making, we use a comprehensive state representation including both the current observation and the historical experience. We train the agent using the actor-critic architecture in an end-to-end manner. The agent is evaluated on several popular unseen cropping datasets. Experiment results show that our method achieves the state-of-the-art performance with much fewer candidate windows and much less time compared with previous weakly supervised methods.