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MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints
Apr 16, 2024
This work proposes a novel learning framework for visual hand dynamics analysis that takes into account the physiological aspects of hand motion. The existing models, which are simplified joint-actuated systems, often produce unnatural motions. To address this, we integrate a musculoskeletal system with a learnable parametric hand model, MANO, to create a new model, MS-MANO. This model emulates the dynamics of muscles and tendons to drive the skeletal system, imposing physiologically realistic constraints on the resulting torque trajectories. We further propose a simulation-in-the-loop pose refinement framework, BioPR, that refines the initial estimated pose through a multi-layer perceptron (MLP) network. Our evaluation of the accuracy of MS-MANO and the efficacy of the BioPR is conducted in two separate parts. The accuracy of MS-MANO is compared with MyoSuite, while the efficacy of BioPR is benchmarked against two large-scale public datasets and two recent state-of-the-art methods. The results demonstrate that our approach consistently improves the baseline methods both quantitatively and qualitatively.
3D-COCO: extension of MS-COCO dataset for image detection and 3D reconstruction modules
Apr 08, 2024We introduce 3D-COCO, an extension of the original MS-COCO dataset providing 3D models and 2D-3D alignment annotations. 3D-COCO was designed to achieve computer vision tasks such as 3D reconstruction or image detection configurable with textual, 2D image, and 3D CAD model queries. We complete the existing MS-COCO dataset with 28K 3D models collected on ShapeNet and Objaverse. By using an IoU-based method, we match each MS-COCO annotation with the best 3D models to provide a 2D-3D alignment. The open-source nature of 3D-COCO is a premiere that should pave the way for new research on 3D-related topics. The dataset and its source codes is available at https://kalisteo.cea.fr/index.php/coco3d-object-detection-and-reconstruction/
Memory Sharing for Large Language Model based Agents
Apr 15, 2024In the realm of artificial intelligence, the adaptation of Large Language Model (LLM)-based agents to execute tasks via natural language prompts represents a significant advancement, notably eliminating the need for explicit retraining or fine tuning for fixed-answer tasks such as common sense questions and yes/no queries. However, the application of In-context Learning to open-ended challenges, such as poetry creation, reveals substantial limitations due to the comprehensiveness of the provided examples and agent's ability to understand the content expressed in the problem, leading to outputs that often diverge significantly from expected results. Addressing this gap, our study introduces the Memory-Sharing (MS) framework for LLM multi-agents, which utilizes a real-time memory storage and retrieval system to enhance the In-context Learning process. Each "memory" within this system captures both the posed query and the corresponding real-time response from an LLM-based agent, aggregating these memories from a broad spectrum of similar agents to enrich the memory pool shared by all agents. This framework not only aids agents in identifying the most relevant examples for specific tasks but also evaluates the potential utility of their memories for future applications by other agents. Empirical validation across three distinct domains involving specialized functions of agents demonstrates that the MS framework significantly improve the agent's performance regrading the open-ended questions. Furthermore, we also discuss what type of memory pool and what retrieval strategy in MS can better help agents, offering a future develop direction of MS. The code and data are available at: https://github.com/GHupppp/MemorySharingLLM
Quality of Experience Oriented Cross-layer Optimization for Real-time XR Video Transmission
Apr 15, 2024Extended reality (XR) is one of the most important applications of beyond 5G and 6G networks. Real-time XR video transmission presents challenges in terms of data rate and delay. In particular, the frame-by-frame transmission mode of XR video makes real-time XR video very sensitive to dynamic network environments. To improve the users' quality of experience (QoE), we design a cross-layer transmission framework for real-time XR video. The proposed framework allows the simple information exchange between the base station (BS) and the XR server, which assists in adaptive bitrate and wireless resource scheduling. We utilize the cross-layer information to formulate the problem of maximizing user QoE by finding the optimal scheduling and bitrate adjustment strategies. To address the issue of mismatched time scales between two strategies, we decouple the original problem and solve them individually using a multi-agent-based approach. Specifically, we propose the multi-step Deep Q-network (MS-DQN) algorithm to obtain a frame-priority-based wireless resource scheduling strategy and then propose the Transformer-based Proximal Policy Optimization (TPPO) algorithm for video bitrate adaptation. The experimental results show that the TPPO+MS-DQN algorithm proposed in this study can improve the QoE by 3.6% to 37.8%. More specifically, the proposed MS-DQN algorithm enhances the transmission quality by 49.9%-80.2%.
FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge
Apr 18, 2024Lexicon-based retrieval has gained siginificant popularity in text retrieval due to its efficient and robust performance. To further enhance performance of lexicon-based retrieval, researchers have been diligently incorporating state-of-the-art methodologies like Neural retrieval and text-level contrastive learning approaches. Nonetheless, despite the promising outcomes, current lexicon-based retrieval methods have received limited attention in exploring the potential benefits of feature context representations and term-level knowledge guidance. In this paper, we introduce an innovative method by introducing FEature Context and TErm-level Knowledge modules(FecTek). To effectively enrich the feature context representations of term weight, the Feature Context Module (FCM) is introduced, which leverages the power of BERT's representation to determine dynamic weights for each element in the embedding. Additionally, we develop a term-level knowledge guidance module (TKGM) for effectively utilizing term-level knowledge to intelligently guide the modeling process of term weight. Evaluation of the proposed method on MS Marco benchmark demonstrates its superiority over the previous state-of-the-art approaches.
6Img-to-3D: Few-Image Large-Scale Outdoor Driving Scene Reconstruction
Apr 18, 2024Current 3D reconstruction techniques struggle to infer unbounded scenes from a few images faithfully. Specifically, existing methods have high computational demands, require detailed pose information, and cannot reconstruct occluded regions reliably. We introduce 6Img-to-3D, an efficient, scalable transformer-based encoder-renderer method for single-shot image to 3D reconstruction. Our method outputs a 3D-consistent parameterized triplane from only six outward-facing input images for large-scale, unbounded outdoor driving scenarios. We take a step towards resolving existing shortcomings by combining contracted custom cross- and self-attention mechanisms for triplane parameterization, differentiable volume rendering, scene contraction, and image feature projection. We showcase that six surround-view vehicle images from a single timestamp without global pose information are enough to reconstruct 360$^{\circ}$ scenes during inference time, taking 395 ms. Our method allows, for example, rendering third-person images and birds-eye views. Our code is available at https://github.com/continental/6Img-to-3D, and more examples can be found at our website here https://6Img-to-3D.GitHub.io/.
HLTCOE at TREC 2023 NeuCLIR Track
Apr 11, 2024The HLTCOE team applied PLAID, an mT5 reranker, and document translation to the TREC 2023 NeuCLIR track. For PLAID we included a variety of models and training techniques -- the English model released with ColBERT v2, translate-train~(TT), Translate Distill~(TD) and multilingual translate-train~(MTT). TT trains a ColBERT model with English queries and passages automatically translated into the document language from the MS-MARCO v1 collection. This results in three cross-language models for the track, one per language. MTT creates a single model for all three document languages by combining the translations of MS-MARCO passages in all three languages into mixed-language batches. Thus the model learns about matching queries to passages simultaneously in all languages. Distillation uses scores from the mT5 model over non-English translated document pairs to learn how to score query-document pairs. The team submitted runs to all NeuCLIR tasks: the CLIR and MLIR news task as well as the technical documents task.
TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment
Apr 17, 2024Proprietary large language models (LLMs) have been widely applied in various scenarios. Additionally, deploying LLMs on edge devices is trending for efficiency and privacy reasons. However, edge deployment of proprietary LLMs introduces new security challenges: edge-deployed models are exposed as white-box accessible to users, enabling adversaries to conduct effective model stealing (MS) attacks. Unfortunately, existing defense mechanisms fail to provide effective protection. Specifically, we identify four critical protection properties that existing methods fail to simultaneously satisfy: (1) maintaining protection after a model is physically copied; (2) authorizing model access at request level; (3) safeguarding runtime reverse engineering; (4) achieving high security with negligible runtime overhead. To address the above issues, we propose TransLinkGuard, a plug-and-play model protection approach against model stealing on edge devices. The core part of TransLinkGuard is a lightweight authorization module residing in a secure environment, e.g., TEE. The authorization module can freshly authorize each request based on its input. Extensive experiments show that TransLinkGuard achieves the same security protection as the black-box security guarantees with negligible overhead.
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification
Apr 17, 2024We present a unified framework called deep dependency networks (DDNs) that combines dependency networks and deep learning architectures for multi-label classification, with a particular emphasis on image and video data. The primary advantage of dependency networks is their ease of training, in contrast to other probabilistic graphical models like Markov networks. In particular, when combined with deep learning architectures, they provide an intuitive, easy-to-use loss function for multi-label classification. A drawback of DDNs compared to Markov networks is their lack of advanced inference schemes, necessitating the use of Gibbs sampling. To address this challenge, we propose novel inference schemes based on local search and integer linear programming for computing the most likely assignment to the labels given observations. We evaluate our novel methods on three video datasets (Charades, TACoS, Wetlab) and three image datasets (MS-COCO, PASCAL VOC, NUS-WIDE), comparing their performance with (a) basic neural architectures and (b) neural architectures combined with Markov networks equipped with advanced inference and learning techniques. Our results demonstrate the superiority of our new DDN methods over the two competing approaches.
Sketch-guided Image Inpainting with Partial Discrete Diffusion Process
Apr 18, 2024In this work, we study the task of sketch-guided image inpainting. Unlike the well-explored natural language-guided image inpainting, which excels in capturing semantic details, the relatively less-studied sketch-guided inpainting offers greater user control in specifying the object's shape and pose to be inpainted. As one of the early solutions to this task, we introduce a novel partial discrete diffusion process (PDDP). The forward pass of the PDDP corrupts the masked regions of the image and the backward pass reconstructs these masked regions conditioned on hand-drawn sketches using our proposed sketch-guided bi-directional transformer. The proposed novel transformer module accepts two inputs -- the image containing the masked region to be inpainted and the query sketch to model the reverse diffusion process. This strategy effectively addresses the domain gap between sketches and natural images, thereby, enhancing the quality of inpainting results. In the absence of a large-scale dataset specific to this task, we synthesize a dataset from the MS-COCO to train and extensively evaluate our proposed framework against various competent approaches in the literature. The qualitative and quantitative results and user studies establish that the proposed method inpaints realistic objects that fit the context in terms of the visual appearance of the provided sketch. To aid further research, we have made our code publicly available at https://github.com/vl2g/Sketch-Inpainting .