Image token removal is an efficient augmentation strategy for reducing the cost of computing image features. However, this efficient augmentation strategy has been found to adversely affect the accuracy of CLIP-based training. We hypothesize that removing a large portion of image tokens may improperly discard the semantic content associated with a given text description, thus constituting an incorrect pairing target in CLIP training. To address this issue, we propose an attentive token removal approach for CLIP training, which retains tokens with a high semantic correlation to the text description. The correlation scores are computed in an online fashion using the EMA version of the visual encoder. Our experiments show that the proposed attentive masking approach performs better than the previous method of random token removal for CLIP training. The approach also makes it efficient to apply multiple augmentation views to the image, as well as introducing instance contrastive learning tasks between these views into the CLIP framework. Compared to other CLIP improvements that combine different pre-training targets such as SLIP and MaskCLIP, our method is not only more effective, but also much more efficient. Specifically, using ViT-B and YFCC-15M dataset, our approach achieves $43.9\%$ top-1 accuracy on ImageNet-1K zero-shot classification, as well as $62.7/42.1$ and $38.0/23.2$ I2T/T2I retrieval accuracy on Flickr30K and MS COCO, which are $+1.1\%$, $+5.5/+0.9$, and $+4.4/+1.3$ higher than the SLIP method, while being $2.30\times$ faster. An efficient version of our approach running $1.16\times$ faster than the plain CLIP model achieves significant gains of $+5.3\%$, $+11.3/+8.0$, and $+9.5/+4.9$ on these benchmarks.

During the deployment of deep neural networks (DNNs) on edge devices, many research efforts are devoted to the limited hardware resource. However, little attention is paid to the influence of dynamic power management. As edge devices typically only have a budget of energy with batteries (rather than almost unlimited energy support on servers or workstations), their dynamic power management often changes the execution frequency as in the widely-used dynamic voltage and frequency scaling (DVFS) technique. This leads to highly unstable inference speed performance, especially for computation-intensive DNN models, which can harm user experience and waste hardware resources. We firstly identify this problem and then propose All-in-One, a highly representative pruning framework to work with dynamic power management using DVFS. The framework can use only one set of model weights and soft masks (together with other auxiliary parameters of negligible storage) to represent multiple models of various pruning ratios. By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i.e., keeping the difference in speed performance under various execution frequencies as small as possible. Our experiments demonstrate that our method not only achieves high accuracy for multiple models of different pruning ratios, but also reduces their variance of inference latency for various frequencies, with minimal memory consumption of only one model and one soft mask.

Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i.e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i.e., opioid use), and evaluate the extraction rate of SDoH using cancer populations. Methods: We identified SDoH categories and attributes and developed an SDoH corpus using clinical notes from a general cancer cohort. We compared four transformer-based NLP models to extract SDoH, examined the generalizability of NLP models to a cohort of patients prescribed with opioids, and explored customization strategies to improve performance. We applied the best NLP model to extract 19 categories of SDoH from the breast (n=7,971), lung (n=11,804), and colorectal cancer (n=6,240) cohorts. Results and Conclusion: We developed a corpus of 629 cancer patients notes with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH. The Bidirectional Encoder Representations from Transformers (BERT) model achieved the best strict/lenient F1 scores of 0.9216 and 0.9441 for SDoH concept extraction, 0.9617 and 0.9626 for linking attributes to SDoH concepts. Fine-tuning the NLP models using new annotations from opioid use patients improved the strict/lenient F1 scores from 0.8172/0.8502 to 0.8312/0.8679. The extraction rates among 19 categories of SDoH varied greatly, where 10 SDoH could be extracted from >70% of cancer patients, but 9 SDoH had a low extraction rate (<70% of cancer patients). The SODA package with pre-trained transformer models is publicly available at https://github.com/uf-hobiinformatics-lab/SDoH_SODA.

Virtual reality and augmented reality (XR) bring increasing demand for 3D content. However, creating high-quality 3D content requires tedious work that a human expert must do. In this work, we study the challenging task of lifting a single image to a 3D object and, for the first time, demonstrate the ability to generate a plausible 3D object with 360{\deg} views that correspond well with the given reference image. By conditioning on the reference image, our model can fulfill the everlasting curiosity for synthesizing novel views of objects from images. Our technique sheds light on a promising direction of easing the workflows for 3D artists and XR designers. We propose a novel framework, dubbed NeuralLift-360, that utilizes a depth-aware neural radiance representation (NeRF) and learns to craft the scene guided by denoising diffusion models. By introducing a ranking loss, our NeuralLift-360 can be guided with rough depth estimation in the wild. We also adopt a CLIP-guided sampling strategy for the diffusion prior to provide coherent guidance. Extensive experiments demonstrate that our NeuralLift-360 significantly outperforms existing state-of-the-art baselines. Project page: https://vita-group.github.io/NeuralLift-360/

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Implicit Neural Representations (INRs) encoding continuous multi-media data via multi-layer perceptrons has shown undebatable promise in various computer vision tasks. Despite many successful applications, editing and processing an INR remains intractable as signals are represented by latent parameters of a neural network. Existing works manipulate such continuous representations via processing on their discretized instance, which breaks down the compactness and continuous nature of INR. In this work, we present a pilot study on the question: how to directly modify an INR without explicit decoding? We answer this question by proposing an implicit neural signal processing network, dubbed INSP-Net, via differential operators on INR. Our key insight is that spatial gradients of neural networks can be computed analytically and are invariant to translation, while mathematically we show that any continuous convolution filter can be uniformly approximated by a linear combination of high-order differential operators. With these two knobs, INSP-Net instantiates the signal processing operator as a weighted composition of computational graphs corresponding to the high-order derivatives of INRs, where the weighting parameters can be data-driven learned. Based on our proposed INSP-Net, we further build the first Convolutional Neural Network (CNN) that implicitly runs on INRs, named INSP-ConvNet. Our experiments validate the expressiveness of INSP-Net and INSP-ConvNet in fitting low-level image and geometry processing kernels (e.g. blurring, deblurring, denoising, inpainting, and smoothening) as well as for high-level tasks on implicit fields such as image classification.

神经体积表示表明，MLP网络可以通过多视图校准图像来训练MLP网络，以表示场景的几何形状和外观，而无需显式3D监督。对象分割可以根据学习的辐射字段丰富许多下游应用程序。但是，引入手工制作的细分以在复杂的现实世界中定义感兴趣的区域是非平凡且昂贵的，因为它获得了每个视图注释。本文使用NERF进行复杂的现实世界场景来探索对物体分割的自我监督学习。我们的框架，nerf-sos，夫妻对象分割和神经辐射字段，以在场景中的任何视图中分割对象。通过提出一种新颖的合作对比度损失，在外观和几何水平上，NERF-SOS鼓励NERF模型将紧凑的几何学分割簇从其密度字段中提炼出紧凑的几何学分割簇以及自我监督的预训练的预训练的2D视觉特征。可以将自我监督的对象分割框架应用于各种NERF模型，这些模型既可以导致室内和室外场景的照片真实的渲染结果和令人信服的分割。 LLFF，坦克和寺庙数据集的广泛结果验证了NERF-SOS的有效性。它始终超过其他基于图像的自我监督基线，甚至比监督的语义nerf捕捉细节。

预先训练的语言模型在多大程度上了解有关分发性现象的语义知识？在本文中，我们介绍了Distnli，这是一种新的自然语言推理诊断数据集，该数据集针对分布式引起的语义差异，并采用因果中介分析框架来量化模型行为并探索该语义相关任务中的基本机制。我们发现，模型的理解程度与模型大小和词汇大小有关。我们还提供有关模型如何编码这种高级语义知识的见解。

成像检查（例如胸部X射线照相）将产生一小部分常见发现和一组少数罕见的发现。虽然训练有素的放射科医生可以通过研究一些代表性的例子来学习罕见条件的视觉呈现，但是教机器从这种“长尾”分布中学习的情况更加困难，因为标准方法很容易偏向最常见的类别。在本文中，我们介绍了胸部X射线胸腔疾病特定领域的长尾学习问题的全面基准研究。我们专注于从自然分布的胸部X射线数据中学习，不仅优化了分类精度，不仅是常见的“头”类，而且还优化了罕见但至关重要的“尾巴”类。为此，我们引入了一个具有挑战性的新长尾X射线基准，以促进开发长尾学习方法进行医学图像分类。该基准由两个用于19-和20向胸部疾病分类的胸部X射线数据集组成，其中包含多达53,000的类别，只有7个标记的训练图像。我们在这种新的基准上评估了标准和最先进的长尾学习方法，分析这些方法的哪些方面对长尾医学图像分类最有益，并总结了对未来算法设计的见解。数据集，训练有素的模型和代码可在https://github.com/vita-group/longtailcxr上找到。

近年来，基于深度学习的模型在视频超分辨率（VSR）方面取得了显着性能，但是这些模型中的大多数不适用于在线视频应用程序。这些方法仅考虑失真质量，而忽略了在线应用程序的关键要求，例如低延迟和模型较低的复杂性。在本文中，我们专注于在线视频传输，其中需要VSR算法来实时生成高分辨率的视频序列。为了应对此类挑战，我们提出了一种基于一种新的内核知识转移方法，称为卷积核旁路移植物（CKBG）。首先，我们设计了一个轻巧的网络结构，该结构不需要将来的帧作为输入，并节省了缓存这些帧的额外时间成本。然后，我们提出的CKBG方法通过用``核移植物）''绕过原始网络来增强这种轻巧的基础模型，这些网络是包含外部预验证图像SR模型的先验知识的额外卷积内核。在测试阶段，我们通过将其转换为简单的单路结构来进一步加速移植的多支球网络。实验结果表明，我们提出的方法可以处理高达110 fps的在线视频序列，并且模型复杂性非常低和竞争性SR性能。