Recently, several methods have been proposed to estimate 3D human pose from multi-view images and achieved impressive performance on public datasets collected in relatively easy scenarios. However, there are limited approaches for extracting 3D human skeletons from multimodal inputs (e.g., RGB and pointcloud) that can enhance the accuracy of predicting 3D poses in challenging situations. We fill this gap by introducing a pipeline called PointVoxel that fuses multi-view RGB and pointcloud inputs to obtain 3D human poses. We demonstrate that volumetric representation is an effective architecture for integrating these different modalities. Moreover, in order to overcome the challenges of annotating 3D human pose labels in difficult scenarios, we develop a synthetic dataset generator for pretraining and design an unsupervised domain adaptation strategy so that we can obtain a well-trained 3D human pose estimator without using any manual annotations. We evaluate our approach on four datasets (two public datasets, one synthetic dataset, and one challenging dataset named BasketBall collected by ourselves), showing promising results. The code and dataset will be released soon.
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Human pose estimation, the process of identifying joint positions in a person's body from images or videos, represents a widely utilized technology across diverse fields, including healthcare. One such healthcare application involves in-bed pose estimation, where the body pose of an individual lying under a blanket is analyzed. This task, for instance, can be used to monitor a person's sleep behavior and detect symptoms early for potential disease diagnosis in homes and hospitals. Several studies have utilized unimodal and multimodal methods to estimate in-bed human poses. The unimodal studies generally employ RGB images, whereas the multimodal studies use modalities including RGB, long-wavelength infrared, pressure map, and depth map. Multimodal studies have the advantage of using modalities in addition to RGB that might capture information useful to cope with occlusions. Moreover, some multimodal studies exclude RGB and, this way, better suit privacy preservation. To expedite advancements in this domain, we conduct a review of existing datasets and approaches. Our objectives are to show the limitations of the previous studies, current challenges, and provide insights for future works on the in-bed human pose estimation field.
Existing learned video compression models employ flow net or deformable convolutional networks (DCN) to estimate motion information. However, the limited receptive fields of flow net and DCN inherently direct their attentiveness towards the local contexts. Global contexts, such as large-scale motions and global correlations among frames are ignored, presenting a significant bottleneck for capturing accurate motions. To address this issue, we propose a joint local and global motion compensation module (LGMC) for leaned video coding. More specifically, we adopt flow net for local motion compensation. To capture global context, we employ the cross attention in feature domain for motion compensation. In addition, to avoid the quadratic complexity of vanilla cross attention, we divide the softmax operations in attention into two independent softmax operations, leading to linear complexity. To validate the effectiveness of our proposed LGMC, we integrate it with DCVC-TCM and obtain learned video compression with joint local and global motion compensation (LVC-LGMC). Extensive experiments demonstrate that our LVC-LGMC has significant rate-distortion performance improvements over baseline DCVC-TCM.
In patent prosecution, timely and effective responses to Office Actions (OAs) are crucial for acquiring patents, yet past automation and AI research have scarcely addressed this aspect. To address this gap, our study introduces the Patent Office Action Response Intelligence System (PARIS) and its advanced version, the Large Language Model Enhanced PARIS (LE-PARIS). These systems are designed to expedite the efficiency of patent attorneys in collaboratively handling OA responses. The systems' key features include the construction of an OA Topics Database, development of Response Templates, and implementation of Recommender Systems and LLM-based Response Generation. Our validation involves a multi-paradigmatic analysis using the USPTO Office Action database and longitudinal data of attorney interactions with our systems over six years. Through five studies, we examine the constructiveness of OA topics (studies 1 and 2) using topic modeling and the proposed Delphi process, the efficacy of our proposed hybrid recommender system tailored for OA (both LLM-based and non-LLM-based) (study 3), the quality of response generation (study 4), and the practical value of the systems in real-world scenarios via user studies (study 5). Results demonstrate that both PARIS and LE-PARIS significantly meet key metrics and positively impact attorney performance.
Secure two-party computation with homomorphic encryption (HE) protects data privacy with a formal security guarantee but suffers from high communication overhead. While previous works, e.g., Cheetah, Iron, etc, have proposed efficient HE-based protocols for different neural network (NN) operations, they still assume high precision, e.g., fixed point 37 bit, for the NN operations and ignore NNs' native robustness against quantization error. In this paper, we propose HEQuant, which features low-precision-quantization-aware optimization for the HE-based protocols. We observe the benefit of a naive combination of quantization and HE quickly saturates as bit precision goes down. Hence, to further improve communication efficiency, we propose a series of optimizations, including an intra-coefficient packing algorithm and a quantization-aware tiling algorithm, to simultaneously reduce the number and precision of the transferred data. Compared with prior-art HE-based protocols, e.g., CrypTFlow2, Cheetah, Iron, etc, HEQuant achieves $3.5\sim 23.4\times$ communication reduction and $3.0\sim 9.3\times$ latency reduction. Meanwhile, when compared with prior-art network optimization frameworks, e.g., SENet, SNL, etc, HEQuant also achieves $3.1\sim 3.6\times$ communication reduction.
Our hands serve as a fundamental means of interaction with the world around us. Therefore, understanding hand poses and interaction context is critical for human-computer interaction. We present EchoWrist, a low-power wristband that continuously estimates 3D hand pose and recognizes hand-object interactions using active acoustic sensing. EchoWrist is equipped with two speakers emitting inaudible sound waves toward the hand. These sound waves interact with the hand and its surroundings through reflections and diffractions, carrying rich information about the hand's shape and the objects it interacts with. The information captured by the two microphones goes through a deep learning inference system that recovers hand poses and identifies various everyday hand activities. Results from the two 12-participant user studies show that EchoWrist is effective and efficient at tracking 3D hand poses and recognizing hand-object interactions. Operating at 57.9mW, EchoWrist is able to continuously reconstruct 20 3D hand joints with MJEDE of 4.81mm and recognize 12 naturalistic hand-object interactions with 97.6% accuracy.
Neural language models are probabilistic models of human text. They are predominantly trained using maximum likelihood estimation (MLE), which is equivalent to minimizing the forward cross-entropy between the empirical data distribution and the model distribution. However, various degeneration phenomena are still widely observed when decoding from the distributions learned by such models. We establish that the forward cross-entropy is suboptimal as a distance metric for aligning human and model distribution due to its (1) recall-prioritization (2) negative diversity ignorance and (3) train-test mismatch. In this paper, we propose Earth Mover Distance Optimization (EMO) for auto-regressive language modeling. EMO capitalizes on the inherent properties of earth mover distance to address the aforementioned challenges. Due to the high complexity of direct computation, we further introduce a feasible upper bound for EMO to ease end-to-end training. Upon extensive evaluation of language models trained using EMO and MLE. We find that EMO demonstrates a consistently better language modeling performance than MLE across domains. Moreover, EMO demonstrates noteworthy enhancements in downstream performance with minimal fine-tuning on merely 25,000 sentences. This highlights the tremendous potential of EMO as a lightweight calibration method for enhancing large-scale pre-trained language models.
To reconstruct a 3D human surface from a single image, it is important to consider human pose, shape and clothing details simultaneously. In recent years, a combination of parametric body models (such as SMPL) that capture body pose and shape prior, and neural implicit functions that learn flexible clothing details, has been used to integrate the advantages of both approaches. However, the combined representation introduces additional computation, e.g. signed distance calculation, in 3D body feature extraction, which exacerbates the redundancy of the implicit query-and-infer process and fails to preserve the underlying body shape prior. To address these issues, we propose a novel IUVD-Feedback representation, which consists of an IUVD occupancy function and a feedback query algorithm. With this representation, the time-consuming signed distance calculation is replaced by a simple linear transformation in the IUVD space, leveraging the SMPL UV maps. Additionally, the redundant query points in the query-and-infer process are reduced through a feedback mechanism. This leads to more reasonable 3D body features and more effective query points, successfully preserving the parametric body prior. Moreover, the IUVD-Feedback representation can be embedded into any existing implicit human reconstruction pipelines without modifying the trained neural networks. Experiments on THuman2.0 dataset demonstrate that the proposed IUVD-Feedback representation improves result robustness and achieves three times faster acceleration in the query-and-infer process. Furthermore, this representation has the potential to be used in generative applications by leveraging its inherited semantic information from the parametric body model.
Ensuring alignment, which refers to making models behave in accordance with human intentions [1,2], has become a critical task before deploying large language models (LLMs) in real-world applications. For instance, OpenAI devoted six months to iteratively aligning GPT-4 before its release [3]. However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations. This obstacle hinders systematic iteration and deployment of LLMs. To address this issue, this paper presents a comprehensive survey of key dimensions that are crucial to consider when assessing LLM trustworthiness. The survey covers seven major categories of LLM trustworthiness: reliability, safety, fairness, resistance to misuse, explainability and reasoning, adherence to social norms, and robustness. Each major category is further divided into several sub-categories, resulting in a total of 29 sub-categories. Additionally, a subset of 8 sub-categories is selected for further investigation, where corresponding measurement studies are designed and conducted on several widely-used LLMs. The measurement results indicate that, in general, more aligned models tend to perform better in terms of overall trustworthiness. However, the effectiveness of alignment varies across the different trustworthiness categories considered. This highlights the importance of conducting more fine-grained analyses, testing, and making continuous improvements on LLM alignment. By shedding light on these key dimensions of LLM trustworthiness, this paper aims to provide valuable insights and guidance to practitioners in the field. Understanding and addressing these concerns will be crucial in achieving reliable and ethically sound deployment of LLMs in various applications.
Diffusion models (DMs) have shown great potential for high-quality image synthesis. However, when it comes to producing images with complex scenes, how to properly describe both image global structures and object details remains a challenging task. In this paper, we present Frido, a Feature Pyramid Diffusion model performing a multi-scale coarse-to-fine denoising process for image synthesis. Our model decomposes an input image into scale-dependent vector quantized features, followed by a coarse-to-fine gating for producing image output. During the above multi-scale representation learning stage, additional input conditions like text, scene graph, or image layout can be further exploited. Thus, Frido can be also applied for conditional or cross-modality image synthesis. We conduct extensive experiments over various unconditioned and conditional image generation tasks, ranging from text-to-image synthesis, layout-to-image, scene-graph-to-image, to label-to-image. More specifically, we achieved state-of-the-art FID scores on five benchmarks, namely layout-to-image on COCO and OpenImages, scene-graph-to-image on COCO and Visual Genome, and label-to-image on COCO. Code is available at //github.com/davidhalladay/Frido.
Multi-agent influence diagrams (MAIDs) are a popular form of graphical model that, for certain classes of games, have been shown to offer key complexity and explainability advantages over traditional extensive form game (EFG) representations. In this paper, we extend previous work on MAIDs by introducing the concept of a MAID subgame, as well as subgame perfect and trembling hand perfect equilibrium refinements. We then prove several equivalence results between MAIDs and EFGs. Finally, we describe an open source implementation for reasoning about MAIDs and computing their equilibria.
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