视频时间基础（VTG）的目标是根据自然语言（NL）描述在未修剪视频中定位时间矩。由于现实世界的应用程序提供了永无止境的视频流，因此它提出了对长形视频的时间基础的需求，这导致了两个主要挑战：（1）长视频长度使得很难处理整个视频而不减少样本速率并导致高计算负担； （2）随着候选时间的增加数量，准确的多模式对准更具挑战性。为了应对这些挑战，我们提出了一个有效的以窗户为中心的粗略对齐框架，它可以灵活地处理具有较高推理速度的长格式视频输入，并通过我们的新颖的Choce-Fine Muly-Fine增强了时间基础模态对齐框架。具体来说，我们通过滑动窗口方法将长视频将长视频切成候选窗口。 Cone（1）以窗户为中心，通过对比度学习和通过对NL查询相关的候选窗口进行过滤来学习窗口间的（粗粒）语义差异，并且（2）执行内部（罚款） - 使用强大的对比视力文本预训练模型的强大多模式对齐能力对候选力矩进行排名。长期视频的两个大规模VTG基准测试的广泛实验始终显示出可观的性能增长（MAD的3.13％至6.87％，从10.46％到EGO4D-NLQ上的10.46％至13.46％），并且Cone在两个数据集上都可以达到SOTA结果。分析揭示了组件的有效性和长期视频接地的效率较高，因为我们的系统在EGO4D-NLQ上提高了2倍的推理速度，而在MAD上提高了15倍的速度，同时保持了锥体的SOTA性能。

拍卖设计中的主要问题之一是开发一种兼容激励兼容的机制，可最大程度地提高拍卖师的预期收入。尽管理论方法在多项目拍卖中遇到了瓶颈，但最近在通过深度学习找到最佳机制方面取得了很多进展。但是，这些作品要么着重于固定的竞标者和项目，要么将拍卖限制为对称。在这项工作中，我们通过将投标人和项目的上下文信息考虑到拍卖学习框架中来克服此类限制。我们提出了$ \ mathtt {Citransnet} $，这是一种基于上下文集成变压器的神经网络，用于最佳拍卖设计，该网络在竞标和上下文上保持了置换率 - 等值，同时能够找到不对称的解决方案。我们通过广泛的实验表明，$ \ mathtt {citransnet} $可以在单项设置中恢复已知的最佳解决方案，在多项目拍卖中优于强大的基线，并且可以很好地推广到培训中的案例以外的其他案例。

本文研究了知识图的推荐系统，可以有效地解决数据稀疏和冷启动的问题。最近，已经为这个问题开发了各种方法，这通常试图根据其表示，学习用户和物品的有效陈述，然后根据其表示将项目匹配。虽然这些方法已经表现得非常有效，但它们缺乏良好的解释，这对推荐系统至关重要。在本文中，我们采取了不同的路线，并提出通过从用户到项目的有意义路径来创造建议。具体地，我们将问题作为顺序决策过程，其中目标用户被定义为初始状态，并且图中的边缘被定义为动作。我们根据现有的最先进方法塑造奖励，然后使用策略梯度方法培训策略函数。三个现实世界数据集的实验结果表明，我们的提出方法不仅提供有效的建议，还提供了良好的解释。

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is of high possibility to be degraded due to noises and distortions. In this paper, we propose two novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (i.e., signal and object)-domain curvature regularization model. Fast numerical optimization algorithms are developed relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, which are further accelerated by GPU implementation. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.

In this paper, we target at the problem of learning a generalizable dynamic radiance field from monocular videos. Different from most existing NeRF methods that are based on multiple views, monocular videos only contain one view at each timestamp, thereby suffering from ambiguity along the view direction in estimating point features and scene flows. Previous studies such as DynNeRF disambiguate point features by positional encoding, which is not transferable and severely limits the generalization ability. As a result, these methods have to train one independent model for each scene and suffer from heavy computational costs when applying to increasing monocular videos in real-world applications. To address this, We propose MonoNeRF to simultaneously learn point features and scene flows with point trajectory and feature correspondence constraints across frames. More specifically, we learn an implicit velocity field to estimate point trajectory from temporal features with Neural ODE, which is followed by a flow-based feature aggregation module to obtain spatial features along the point trajectory. We jointly optimize temporal and spatial features by training the network in an end-to-end manner. Experiments show that our MonoNeRF is able to learn from multiple scenes and support new applications such as scene editing, unseen frame synthesis, and fast novel scene adaptation.

The advance of computer-aided detection systems using deep learning opened a new scope in endoscopic image analysis. However, the learning-based models developed on closed datasets are susceptible to unknown anomalies in complex clinical environments. In particular, the high false positive rate of polyp detection remains a major challenge in clinical practice. In this work, we release the FPPD-13 dataset, which provides a taxonomy and real-world cases of typical false positives during computer-aided polyp detection in real-world colonoscopy. We further propose a post-hoc module EndoBoost, which can be plugged into generic polyp detection models to filter out false positive predictions. This is realized by generative learning of the polyp manifold with normalizing flows and rejecting false positives through density estimation. Compared to supervised classification, this anomaly detection paradigm achieves better data efficiency and robustness in open-world settings. Extensive experiments demonstrate a promising false positive suppression in both retrospective and prospective validation. In addition, the released dataset can be used to perform 'stress' tests on established detection systems and encourages further research toward robust and reliable computer-aided endoscopic image analysis. The dataset and code will be publicly available at http://endoboost.miccai.cloud.

In this paper, we propose a large-scale language pre-training for text GENeration using dIffusion modEl, which is named GENIE. GENIE is a pre-training sequence-to-sequence text generation model which combines Transformer and diffusion. The diffusion model accepts the latent information from the encoder, which is used to guide the denoising of the current time step. After multiple such denoise iterations, the diffusion model can restore the Gaussian noise to the diverse output text which is controlled by the input text. Moreover, such architecture design also allows us to adopt large scale pre-training on the GENIE. We propose a novel pre-training method named continuous paragraph denoise based on the characteristics of the diffusion model. Extensive experiments on the XSum, CNN/DailyMail, and Gigaword benchmarks shows that GENIE can achieves comparable performance with various strong baselines, especially after pre-training, the generation quality of GENIE is greatly improved. We have also conduct a lot of experiments on the generation diversity and parameter impact of GENIE. The code for GENIE will be made publicly available.

Structured tabular data exist across nearly all fields. Reasoning task over these data aims to answer questions or determine the truthiness of hypothesis sentences by understanding the semantic meaning of a table. While previous works have devoted significant efforts to the tabular reasoning task, they always assume there are sufficient labeled data. However, constructing reasoning samples over tables (and related text) is labor-intensive, especially when the reasoning process is complex. When labeled data is insufficient, the performance of models will suffer an unendurable decline. In this paper, we propose a unified framework for unsupervised complex tabular reasoning (UCTR), which generates sufficient and diverse synthetic data with complex logic for tabular reasoning tasks, assuming no human-annotated data at all. We first utilize a random sampling strategy to collect diverse programs of different types and execute them on tables based on a "Program-Executor" module. To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs. Since a table often occurs with its surrounding texts, we further propose novel "Table-to-Text" and "Text-to-Table" operators to handle joint table-text reasoning scenarios. This way, we can adequately exploit the unlabeled table resources to obtain a well-performed reasoning model under an unsupervised setting. Our experiments cover different tasks (question answering and fact verification) and different domains (general and specific), showing that our unsupervised methods can achieve at most 93% performance compared to supervised models. We also find that it can substantially boost the supervised performance in low-resourced domains as a data augmentation technique. Our code is available at https://github.com/leezythu/UCTR.

Making sense of multiple modalities can yield a more comprehensive description of real-world phenomena. However, learning the co-representation of diverse modalities is still a long-standing endeavor in emerging machine learning applications and research. Previous generative approaches for multimodal input approximate a joint-modality posterior by uni-modality posteriors as product-of-experts (PoE) or mixture-of-experts (MoE). We argue that these approximations lead to a defective bound for the optimization process and loss of semantic connection among modalities. This paper presents a novel variational method on sets called the Set Multimodal VAE (SMVAE) for learning a multimodal latent space while handling the missing modality problem. By modeling the joint-modality posterior distribution directly, the proposed SMVAE learns to exchange information between multiple modalities and compensate for the drawbacks caused by factorization. In public datasets of various domains, the experimental results demonstrate that the proposed method is applicable to order-agnostic cross-modal generation while achieving outstanding performance compared to the state-of-the-art multimodal methods. The source code for our method is available online https://anonymous.4open.science/r/SMVAE-9B3C/.

The dual-encoder has become the de facto architecture for dense retrieval. Typically, it computes the latent representations of the query and document independently, thus failing to fully capture the interactions between the query and document. To alleviate this, recent work expects to get query-informed representations of documents. During training, it expands the document with a real query, while replacing the real query with a generated pseudo query at inference. This discrepancy between training and inference makes the dense retrieval model pay more attention to the query information but ignore the document when computing the document representation. As a result, it even performs worse than the vanilla dense retrieval model, since its performance depends heavily on the relevance between the generated queries and the real query. In this paper, we propose a curriculum sampling strategy, which also resorts to the pseudo query at training and gradually increases the relevance of the generated query to the real query. In this way, the retrieval model can learn to extend its attention from the document only to both the document and query, hence getting high-quality query-informed document representations. Experimental results on several passage retrieval datasets show that our approach outperforms the previous dense retrieval methods1.