Most existing text-video retrieval methods focus on cross-modal matching between the visual content of offline videos and textual query sentences. However, in real scenarios, online videos are frequently accompanied by relevant text information such as titles, tags, and even subtitles, which can be utilized to match textual queries. This inspires us to generate associated captions from offline videos to help with existing text-video retrieval methods. To do so, we propose to use the zero-shot video captioner with knowledge of pre-trained web-scale models (e.g., CLIP and GPT-2) to generate captions for offline videos without any training. Given the captions, one question naturally arises: what can auxiliary captions do for text-video retrieval? In this paper, we present a novel framework Cap4Video, which makes use of captions from three aspects: i) Input data: The video and captions can form new video-caption pairs as data augmentation for training. ii) Feature interaction: We perform feature interaction between video and caption to yield enhanced video representations. iii) Output score: The Query-Caption matching branch can be complementary to the original Query-Video matching branch for text-video retrieval. We conduct thorough ablation studies to demonstrate the effectiveness of our method. Without any post-processing, our Cap4Video achieves state-of-the-art performance on MSR-VTT (51.4%), VATEX (66.6%), MSVD (51.8%), and DiDeMo (52.0%).

Vision-language models (VLMs) that are pre-trained on large-scale image-text pairs have demonstrated impressive transferability on a wide range of visual tasks. Transferring knowledge from such powerful pre-trained VLMs is emerging as a promising direction for building effective video recognition models. However, the current exploration is still limited. In our opinion, the greatest charm of pre-trained vision-language models is to build a bridge between visual and textual domains. In this paper, we present a novel framework called BIKE which utilizes the cross-modal bridge to explore bidirectional knowledge: i) We propose a Video Attribute Association mechanism which leverages the Video-to-Text knowledge to generate textual auxiliary attributes to complement video recognition. ii) We also present a Temporal Concept Spotting mechanism which uses the Text-to-Video expertise to capture temporal saliency in a parameter-free manner to yield enhanced video representation. The extensive studies on popular video datasets (ie, Kinetics-400 & 600, UCF-101, HMDB-51 and ActivityNet) show that our method achieves state-of-the-art performance in most recognition scenarios, eg, general, zero-shot, and few-shot video recognition. To the best of our knowledge, our best model achieves a state-of-the-art accuracy of 88.4% on challenging Kinetics-400 with the released CLIP pre-trained model.

A crucial issue of current text generation models is that they often uncontrollably generate factually inconsistent text with respective of their inputs. Limited by the lack of annotated data, existing works in evaluating factual consistency directly transfer the reasoning ability of models trained on other data-rich upstream tasks like question answering (QA) and natural language inference (NLI) without any further adaptation. As a result, they perform poorly on the real generated text and are biased heavily by their single-source upstream tasks. To alleviate this problem, we propose a weakly supervised framework that aggregates multiple resources to train a precise and efficient factual metric, namely WeCheck. WeCheck first utilizes a generative model to accurately label a real generated sample by aggregating its weak labels, which are inferred from multiple resources. Then, we train the target metric model with the weak supervision while taking noises into consideration. Comprehensive experiments on a variety of tasks demonstrate the strong performance of WeCheck, which achieves a 3.4\% absolute improvement over previous state-of-the-art methods on TRUE benchmark on average.

Surgery is the only viable treatment for cataract patients with visual acuity (VA) impairment. Clinically, to assess the necessity of cataract surgery, accurately predicting postoperative VA before surgery by analyzing multi-view optical coherence tomography (OCT) images is crucially needed. Unfortunately, due to complicated fundus conditions, determining postoperative VA remains difficult for medical experts. Deep learning methods for this problem were developed in recent years. Although effective, these methods still face several issues, such as not efficiently exploring potential relations between multi-view OCT images, neglecting the key role of clinical prior knowledge (e.g., preoperative VA value), and using only regression-based metrics which are lacking reference. In this paper, we propose a novel Cross-token Transformer Network (CTT-Net) for postoperative VA prediction by analyzing both the multi-view OCT images and preoperative VA. To effectively fuse multi-view features of OCT images, we develop cross-token attention that could restrict redundant/unnecessary attention flow. Further, we utilize the preoperative VA value to provide more information for postoperative VA prediction and facilitate fusion between views. Moreover, we design an auxiliary classification loss to improve model performance and assess VA recovery more sufficiently, avoiding the limitation by only using the regression metrics. To evaluate CTT-Net, we build a multi-view OCT image dataset collected from our collaborative hospital. A set of extensive experiments validate the effectiveness of our model compared to existing methods in various metrics. Code is available at: https://github.com/wjh892521292/Cataract OCT.

Photo-realistic style transfer aims at migrating the artistic style from an exemplar style image to a content image, producing a result image without spatial distortions or unrealistic artifacts. Impressive results have been achieved by recent deep models. However, deep neural network based methods are too expensive to run in real-time. Meanwhile, bilateral grid based methods are much faster but still contain artifacts like overexposure. In this work, we propose the \textbf{Adaptive ColorMLP (AdaCM)}, an effective and efficient framework for universal photo-realistic style transfer. First, we find the complex non-linear color mapping between input and target domain can be efficiently modeled by a small multi-layer perceptron (ColorMLP) model. Then, in \textbf{AdaCM}, we adopt a CNN encoder to adaptively predict all parameters for the ColorMLP conditioned on each input content and style image pair. Experimental results demonstrate that AdaCM can generate vivid and high-quality stylization results. Meanwhile, our AdaCM is ultrafast and can process a 4K resolution image in 6ms on one V100 GPU.

Motivation: Enhancers are important cis-regulatory elements that regulate a wide range of biological functions and enhance the transcription of target genes. Although many state-of-the-art computational methods have been proposed in order to efficiently identify enhancers, learning globally contextual features is still one of the challenges for computational methods. Regarding the similarities between biological sequences and natural language sentences, the novel BERT-based language techniques have been applied to extracting complex contextual features in various computational biology tasks such as protein function/structure prediction. To speed up the research on enhancer identification, it is urgent to construct a BERT-based enhancer language model. Results: In this paper, we propose a multi-scale enhancer identification method (iEnhancer-ELM) based on enhancer language models, which treat enhancer sequences as natural language sentences that are composed of k-mer nucleotides. iEnhancer-ELM can extract contextual information of multi-scale k-mers with positions from raw enhancer sequences. Benefiting from the complementary information of k-mers in multi-scale, we ensemble four iEnhancer-ELM models for improving enhancer identification. The benchmark comparisons show that our model outperforms state-of-the-art methods. By the interpretable attention mechanism, we finds 30 biological patterns, where 40% (12/30) are verified by a widely used motif tool (STREME) and a popular dataset (JASPAR), demonstrating our model has a potential ability to reveal the biological mechanism of enhancer. Availability: The source code are available at https://github.com/chen-bioinfo/iEnhancer-ELM Contact: junjiechen@hit.edu.cn and junjie.chen.hit@gmail.com; Supplementary information: Supplementary data are available at Bioinformatics online.

Swarm learning (SL) is an emerging promising decentralized machine learning paradigm and has achieved high performance in clinical applications. SL solves the problem of a central structure in federated learning by combining edge computing and blockchain-based peer-to-peer network. While there are promising results in the assumption of the independent and identically distributed (IID) data across participants, SL suffers from performance degradation as the degree of the non-IID data increases. To address this problem, we propose a generative augmentation framework in swarm learning called SL-GAN, which augments the non-IID data by generating the synthetic data from participants. SL-GAN trains generators and discriminators locally, and periodically aggregation via a randomly elected coordinator in SL network. Under the standard assumptions, we theoretically prove the convergence of SL-GAN using stochastic approximations. Experimental results demonstrate that SL-GAN outperforms state-of-art methods on three real world clinical datasets including Tuberculosis, Leukemia, COVID-19.

使用具有固定尺度的图像超分辨率（SR）的深度学习技术，已经取得了巨大的成功。为了提高其现实世界的适用性，还提出了许多模型来恢复具有任意尺度因子的SR图像，包括不对称的图像，其中图像沿水平和垂直方向大小为不同的尺度。尽管大多数模型仅针对单向上升尺度任务进行了优化，同时假设针对低分辨率（LR）输入的预定义的缩小内核，但基于可逆神经网络（INN）的最新模型能够通过优化降低和降低尺度和降低范围的降低准确性来显着提高上升的准确性共同。但是，受创新体系结构的限制，它被限制在固定的整数尺度因素上，并且需要每个量表的一个模型。在不增加模型复杂性的情况下，提出了一个简单有效的可逆重新恢复网络（IARN），以通过在这项工作中仅训练一个模型来实现任意图像重新缩放。使用创新的组件，例如位置感知量表编码和先发制通道拆分，该网络被优化，以将不可固化的重新恢复周期转换为有效的可逆过程。证明它可以在双向任意重新缩放中实现最新的（SOTA）性能，而不会在LR输出中损害感知质量。还可以证明，使用相同的网络体系结构在不对称尺度的测试上表现良好。

图像文本检索（ITR）在桥接视觉和舌形式方面具有挑战性。对比度学习已被大多数先前的艺术所采用。除了有限的负面图像文本对外，约束学习的能力受到手动加权负对以及对外部知识的不认识的限制。在本文中，我们提出了新型耦合多样性敏感的动量约束学习（编码器），以改善跨模式表示。首先，发明了一种新颖的多样性对比度学习（DCL）体系结构。我们引入了两种模式的动态词典，以扩大图像文本对的比例，并且通过自适应负面对加权实现多样性敏感性。此外，编码器设计了两个分支。一个人从图像/文本中学习实例级的嵌入式，它还基于其嵌入为其输入图像/文本生成伪在线聚类标签。同时，另一个分支学会从常识知识图中查询以形成两种模式的概念级描述符。之后，两个分支都利用DCL来对齐跨模式嵌入空间，而额外的伪聚类标签预测损失则用于促进第二个分支的概念级表示学习。在两个流行的基准测试（即Mscoco和Flicker30k）上进行的广泛实验，验证编码器的表现明显优于最先进的方法。

对于人工智能系统来说，在低计算成本的情况下实现准确的视频识别是一项挑战。基于自适应推理的有效视频识别方法通常会预览视频，并专注于显着零件以降低计算成本。大多数现有作品都集中在复杂的网络学习，并具有基于视频分类的目标。以所有框架为正样本，其中很少有人关注积极样本（显着框架）和负面样本（非空位框架）之间的歧视。为了填补这一空白，在本文中，我们提出了一个新型的非高度抑制网络（NSNET），该网络有效地抑制了非征力框架的响应。具体而言，在框架级别上，可以生成可以区分显着框架和非空位框架的有效伪标签，以指导框架显着性学习。在视频层面上，在双重视频级别的监督下都学会了一个时间关注模块，这些模块既是对突出表示和非偏心表示形式。从两个两个级别的显着度测量都合并以利用多粒性互补信息。在四个众所周知的基准上进行的广泛实验验证了我们的NSNET不仅实现了最先进的准确性效率折衷，而且比最先进的推理速度要快得多（2.4〜4.3倍） - 艺术方法。我们的项目页面位于https://lawrencexia2008.github.io/projects/nsnet。