当相互作用数据稀缺时，深厚的增强学习（RL）算法遭受了严重的性能下降，这限制了其现实世界的应用。最近，视觉表示学习已被证明是有效的，并且有望提高RL样品效率。这些方法通常依靠对比度学习和数据扩展来训练状态预测的过渡模型，这与在RL中使用模型的方式不同 - 基于价值的计划。因此，学到的模型可能无法与环境保持良好状态并产生一致的价值预测，尤其是当国家过渡不是确定性的情况下。为了解决这个问题，我们提出了一种称为价值一致表示学习（VCR）的新颖方法，以学习与决策直接相关的表示形式。更具体地说，VCR训练一个模型，以预测基于当前的状态（也称为“想象的状态”）和一系列动作。 VCR没有将这个想象中的状态与环境返回的真实状态保持一致，而是在两个状态上应用$ q $ - 价值头，并获得了两个行动值分布。然后将距离计算并最小化以迫使想象的状态产生与真实状态相似的动作值预测。我们为离散和连续的动作空间开发了上述想法的两个实现。我们对Atari 100K和DeepMind Control Suite基准测试进行实验，以验证其提高样品效率的有效性。已经证明，我们的方法实现了无搜索RL算法的新最新性能。

The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions usually involve class-balancing strategies, e.g. by loss re-weighting, data re-sampling, or transfer learning from head-to tail-classes, but most of them adhere to the scheme of jointly learning representations and classifiers. In this work, we decouple the learning procedure into representation learning and classification, and systematically explore how different balancing strategies affect them for long-tailed recognition. The findings are surprising: (1) data imbalance might not be an issue in learning high-quality representations; (2) with representations learned with the simplest instance-balanced (natural) sampling, it is also possible to achieve strong long-tailed recognition ability by adjusting only the classifier. We conduct extensive experiments and set new state-of-the-art performance on common long-tailed benchmarks like ImageNet-LT, Places-LT and iNaturalist, showing that it is possible to outperform carefully designed losses, sampling strategies, even complex modules with memory, by using a straightforward approach that decouples representation and classification. Our code is available at https://github.com/facebookresearch/classifier-balancing.

由于其令人鼓舞的性能，在各种控制任务中的令人鼓舞的表现，深增强学习（Deep RL）一直在受到更高的关注。然而，在训练神经网络中的常规正则化技术（例如，$ L_2 $正则化，辍学）已经在RL方法中被忽略，可能是因为代理通常在相同的环境中进行培训和评估，因为Deep RL社区重点关注更多-Level算法设计。在这项工作中，我们在连续控制任务中提出了具有多种策略优化算法的正则化技术的第一综合研究。有趣的是，我们发现策略网络上的传统正则化技术通常可以带来大量改进，特别是在更难的任务上。我们的研究结果显示在训练HyperParameter变化方面是强大的。我们还将这些技术与更广泛使用的熵正则化进行了比较。此外，我们还研究正规化不同的组件，并发现策略网络通常是最佳的。我们进一步分析了为什么正则化可能有助于从四个观点来帮助推广 - 样本复杂性，奖励分配，重量规范和噪音鲁棒性。我们希望我们的研究为未来的规则策略优化算法提供指导。我们的代码可在https://github.com/xuanlinli17/ICLRR2021_RLREG上获得。

最近的图像识别进展刺激了以前所未有的规模部署视觉系统。因此，目前的数据通常不仅由人类而且由机器消耗。现有的图像处理方法仅优化以获得更好的人类感知，但是可以通过机器准确地识别所得到的图像。这可以是不希望的，例如，可以通过搜索引擎或推荐系统来处理图像。在这项工作中，我们研究了简单的方法来提高处理图像的机器识别：直接在图像处理网络上或通过中间变换模型优化识别损耗。有趣的是，加工模型加强识别质量的能力可以在评估不同架构，公认的类别，任务和训练数据集的模型时传输。这使得即使在我们没有未来识别模型的知识，例如，如果我们将被处理的图像上传到Internet的情况，也使得这些方法适用。我们对多种图像处理任务进行实验，用Imagenet分类和Pascal VOC检测作为识别任务。利用这些简单但有效的方法，可以通过强大的可转移性和最小的图像质量损失来实现大量的精度增益。通过用户学习，我们进一步表明精度增益可以转移到黑盒云模型。最后，我们试图通过展示不同模型决策边界的相似之处来解释这种可转移性现象。代码可在https://github.com/liuzhuang13/transferable_ra获得。

Conventional training of a deep CNN based object detector demands a large number of bounding box annotations, which may be unavailable for rare categories. In this work we develop a few-shot object detector that can learn to detect novel objects from only a few annotated examples. Our proposed model leverages fully labeled base classes and quickly adapts to novel classes, using a meta feature learner and a reweighting module within a one-stage detection architecture. The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples. The reweighting module transforms a few support examples from the novel classes to a global vector that indicates the importance or relevance of meta features for detecting the corresponding objects. These two modules, together with a detection prediction module, are trained end-to-end based on an episodic few-shot learning scheme and a carefully designed loss function. Through extensive experiments we demonstrate that our model outperforms well-established baselines by a large margin for few-shot object detection, on multiple datasets and settings. We also present analysis on various aspects of our proposed model, aiming to provide some inspiration for future few-shot detection works.

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-balanced Re-weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 \& V6 show the performances and generality of the SCR with the traditional SGG models.

In the field of cross-modal retrieval, single encoder models tend to perform better than dual encoder models, but they suffer from high latency and low throughput. In this paper, we present a dual encoder model called BagFormer that utilizes a cross modal interaction mechanism to improve recall performance without sacrificing latency and throughput. BagFormer achieves this through the use of bag-wise interactions, which allow for the transformation of text to a more appropriate granularity and the incorporation of entity knowledge into the model. Our experiments demonstrate that BagFormer is able to achieve results comparable to state-of-the-art single encoder models in cross-modal retrieval tasks, while also offering efficient training and inference with 20.72 times lower latency and 25.74 times higher throughput.

Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (<50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.

Three-dimensional (3D) ultrasound imaging technique has been applied for scoliosis assessment, but current assessment method only uses coronal projection image and cannot illustrate the 3D deformity and vertebra rotation. The vertebra detection is essential to reveal 3D spine information, but the detection task is challenging due to complex data and limited annotations. We propose VertMatch, a two-step framework to detect vertebral structures in 3D ultrasound volume by utilizing unlabeled data in semi-supervised manner. The first step is to detect the possible positions of structures on transverse slice globally, and then the local patches are cropped based on detected positions. The second step is to distinguish whether the patches contain real vertebral structures and screen the predicted positions from the first step. VertMatch develops three novel components for semi-supervised learning: for position detection in the first step, (1) anatomical prior is used to screen pseudo labels generated from confidence threshold method; (2) multi-slice consistency is used to utilize more unlabeled data by inputting multiple adjacent slices; (3) for patch identification in the second step, the categories are rebalanced in each batch to solve imbalance problem. Experimental results demonstrate that VertMatch can detect vertebra accurately in ultrasound volume and outperforms state-of-the-art methods. VertMatch is also validated in clinical application on forty ultrasound scans, and it can be a promising approach for 3D assessment of scoliosis.

Image captioning is one of the straightforward tasks that can take advantage of large-scale web-crawled data which provides rich knowledge about the visual world for a captioning model. However, since web-crawled data contains image-text pairs that are aligned at different levels, the inherent noises (e.g., misaligned pairs) make it difficult to learn a precise captioning model. While the filtering strategy can effectively remove noisy data, however, it leads to a decrease in learnable knowledge and sometimes brings about a new problem of data deficiency. To take the best of both worlds, we propose a noise-aware learning framework, which learns rich knowledge from the whole web-crawled data while being less affected by the noises. This is achieved by the proposed quality controllable model, which is learned using alignment levels of the image-text pairs as an additional control signal during training. The alignment-conditioned training allows the model to generate high-quality captions of well-aligned by simply setting the control signal to desired alignment level at inference time. Through in-depth analysis, we show that our controllable captioning model is effective in handling noise. In addition, with two tasks of zero-shot captioning and text-to-image retrieval using generated captions (i.e., self-retrieval), we also demonstrate our model can produce high-quality captions in terms of descriptiveness and distinctiveness. Code is available at \url{https://github.com/kakaobrain/noc}.