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Identity-aware Dual-constraint Network for Cloth-Changing Person Re-identification
Mar 13, 2024
Cloth-Changing Person Re-Identification (CC-ReID) aims to accurately identify the target person in more realistic surveillance scenarios, where pedestrians usually change their clothing. Despite great progress, limited cloth-changing training samples in existing CC-ReID datasets still prevent the model from adequately learning cloth-irrelevant features. In addition, due to the absence of explicit supervision to keep the model constantly focused on cloth-irrelevant areas, existing methods are still hampered by the disruption of clothing variations. To solve the above issues, we propose an Identity-aware Dual-constraint Network (IDNet) for the CC-ReID task. Specifically, to help the model extract cloth-irrelevant clues, we propose a Clothes Diversity Augmentation (CDA), which generates more realistic cloth-changing samples by enriching the clothing color while preserving the texture. In addition, a Multi-scale Constraint Block (MCB) is designed, which extracts fine-grained identity-related features and effectively transfers cloth-irrelevant knowledge. Moreover, a Counterfactual-guided Attention Module (CAM) is presented, which learns cloth-irrelevant features from channel and space dimensions and utilizes the counterfactual intervention for supervising the attention map to highlight identity-related regions. Finally, a Semantic Alignment Constraint (SAC) is designed to facilitate high-level semantic feature interaction. Comprehensive experiments on four CC-ReID datasets indicate that our method outperforms prior state-of-the-art approaches.
AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation
Dec 20, 2023Text-to-3D model adaptations have advanced static 3D model quality, but sequential 3D model generation, particularly for animatable objects with large motions, is still scarce. Our work proposes AnimatableDreamer, a text-to-4D generation framework capable of generating diverse categories of non-rigid objects while adhering to the object motions extracted from a monocular video. At its core, AnimatableDreamer is equipped with our novel optimization design dubbed Canonical Score Distillation (CSD), which simplifies the generation dimension from 4D to 3D by denoising over different frames in the time-varying camera spaces while conducting the distillation process in a unique canonical space shared per video. Concretely, CSD ensures that score gradients back-propagate to the canonical space through differentiable warping, hence guaranteeing the time-consistent generation and maintaining morphological plausibility across different poses. By lifting the 3D generator to 4D with warping functions, AnimatableDreamer offers a novel perspective on non-rigid 3D model generation and reconstruction. Besides, with inductive knowledge from a multi-view consistent diffusion model, CSD regularizes reconstruction from novel views, thus cyclically enhancing the generation process. Extensive experiments demonstrate the capability of our method in generating high-flexibility text-guided 3D models from the monocular video, while also showing improved reconstruction performance over typical non-rigid reconstruction methods. Project page https://AnimatableDreamer.github.io.
A Survey on Robotic Manipulation of Deformable Objects: Recent Advances, Open Challenges and New Frontiers
Dec 16, 2023Deformable object manipulation (DOM) for robots has a wide range of applications in various fields such as industrial, service and health care sectors. However, compared to manipulation of rigid objects, DOM poses significant challenges for robotic perception, modeling and manipulation, due to the infinite dimensionality of the state space of deformable objects (DOs) and the complexity of their dynamics. The development of computer graphics and machine learning has enabled novel techniques for DOM. These techniques, based on data-driven paradigms, can address some of the challenges that analytical approaches of DOM face. However, some existing reviews do not include all aspects of DOM, and some previous reviews do not summarize data-driven approaches adequately. In this article, we survey more than 150 relevant studies (data-driven approaches mainly) and summarize recent advances, open challenges, and new frontiers for aspects of perception, modeling and manipulation for DOs. Particularly, we summarize initial progress made by Large Language Models (LLMs) in robotic manipulation, and indicates some valuable directions for further research. We believe that integrating data-driven approaches and analytical approaches can provide viable solutions to open challenges of DOM.
Robot Learning in the Era of Foundation Models: A Survey
Nov 24, 2023The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning from automation towards general embodied Artificial Intelligence (AI). Adopting foundation models together with traditional learning methods to robot learning has increasingly gained recent interest research community and showed potential for real-life application. However, there are few literatures comprehensively reviewing the relatively new technologies combined with robotics. The purpose of this review is to systematically assess the state-of-the-art foundation model techniques in the robot learning and to identify future potential areas. Specifically, we first summarized the technical evolution of robot learning and identified the necessary preliminary preparations for foundation models including the simulators, datasets, foundation model framework. In addition, we focused on the following four mainstream areas of robot learning including manipulation, navigation, planning, and reasoning and demonstrated how the foundation model techniques can be adopted in the above scenarios. Furthermore, critical issues which are neglected in the current literatures including robot hardware and software decoupling, dynamic data, generalization performance with the presence of human, etc. were discussed. This review highlights the state-of-the-art progress of foundation models in robot learning and future research should focus on multimodal interaction especially dynamics data, exclusive foundation models for robots, and AI alignment, etc.
Implementation of The Future of Drug Discovery: QuantumBased Machine Learning Simulation (QMLS)
Aug 14, 2023
The Research & Development (R&D) phase of drug development is a lengthy and costly process. To revolutionize this process, we introduce our new concept QMLS to shorten the whole R&D phase to three to six months and decrease the cost to merely fifty to eighty thousand USD. For Hit Generation, Machine Learning Molecule Generation (MLMG) generates possible hits according to the molecular structure of the target protein while the Quantum Simulation (QS) filters molecules from the primary essay based on the reaction and binding effectiveness with the target protein. Then, For Lead Optimization, the resultant molecules generated and filtered from MLMG and QS are compared, and molecules that appear as a result of both processes will be made into dozens of molecular variations through Machine Learning Molecule Variation (MLMV), while others will only be made into a few variations. Lastly, all optimized molecules would undergo multiple rounds of QS filtering with a high standard for reaction effectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs. This paper is based on our first paper, where we pitched the concept of machine learning combined with quantum simulations. In this paper we will go over the detailed design and framework of QMLS, including MLMG, MLMV, and QS.
* 13 pages, 6 figures
Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning
Jul 20, 2023
Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to spontaneously set diverse goals to learn a set of skills. Despite the excellent works proposed in various fields, reaching distant goals in temporally extended tasks remains a challenge for GCRL. Current works tackled this problem by leveraging planning algorithms to plan intermediate subgoals to augment GCRL. Their methods need two crucial requirements: (i) a state representation space to search valid subgoals, and (ii) a distance function to measure the reachability of subgoals. However, they struggle to scale to high-dimensional state space due to their non-compact representations. Moreover, they cannot collect high-quality training data through standard GC policies, which results in an inaccurate distance function. Both affect the efficiency and performance of planning and policy learning. In the paper, we propose a goal-conditioned RL algorithm combined with Disentanglement-based Reachability Planning (REPlan) to solve temporally extended tasks. In REPlan, a Disentangled Representation Module (DRM) is proposed to learn compact representations which disentangle robot poses and object positions from high-dimensional observations in a self-supervised manner. A simple REachability discrimination Module (REM) is also designed to determine the temporal distance of subgoals. Moreover, REM computes intrinsic bonuses to encourage the collection of novel states for training. We evaluate our REPlan in three vision-based simulation tasks and one real-world task. The experiments demonstrate that our REPlan significantly outperforms the prior state-of-the-art methods in solving temporally extended tasks.
Communication and Control in Collaborative UAVs: Recent Advances and Future Trends
Feb 23, 2023
The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.
Zero-shot object goal visual navigation
Jun 15, 2022
Object goal visual navigation is a challenging task that aims to guide a robot to find the target object only based on its visual observation, and the target is limited to the classes specified in the training stage. However, in real households, there may exist numerous object classes that the robot needs to deal with, and it is hard for all of these classes to be contained in the training stage. To address this challenge, we propose a zero-shot object navigation task by combining zero-shot learning with object goal visual navigation, which aims at guiding robots to find objects belonging to novel classes without any training samples. This task gives rise to the need to generalize the learned policy to novel classes, which is a less addressed issue of object navigation using deep reinforcement learning. To address this issue, we utilize "class-unrelated" data as input to alleviate the overfitting of the classes specified in the training stage. The class-unrelated input consists of detection results and cosine similarity of word embeddings, and does not contain any class-related visual features or knowledge graphs. Extensive experiments on the AI2-THOR platform show that our model outperforms the baseline models in both seen and unseen classes, which proves that our model is less class-sensitive and generalizes better. Our code is available at https://github.com/pioneer-innovation/Zero-Shot-Object-Navigation
Knowledge-enriched Attention Network with Group-wise Semantic for Visual Storytelling
Mar 10, 2022
As a technically challenging topic, visual storytelling aims at generating an imaginary and coherent story with narrative multi-sentences from a group of relevant images. Existing methods often generate direct and rigid descriptions of apparent image-based contents, because they are not capable of exploring implicit information beyond images. Hence, these schemes could not capture consistent dependencies from holistic representation, impairing the generation of reasonable and fluent story. To address these problems, a novel knowledge-enriched attention network with group-wise semantic model is proposed. Three main novel components are designed and supported by substantial experiments to reveal practical advantages. First, a knowledge-enriched attention network is designed to extract implicit concepts from external knowledge system, and these concepts are followed by a cascade cross-modal attention mechanism to characterize imaginative and concrete representations. Second, a group-wise semantic module with second-order pooling is developed to explore the globally consistent guidance. Third, a unified one-stage story generation model with encoder-decoder structure is proposed to simultaneously train and infer the knowledge-enriched attention network, group-wise semantic module and multi-modal story generation decoder in an end-to-end fashion. Substantial experiments on the popular Visual Storytelling dataset with both objective and subjective evaluation metrics demonstrate the superior performance of the proposed scheme as compared with other state-of-the-art methods.
Weakly Supervised Disentangled Representation for Goal-conditioned Reinforcement Learning
Feb 28, 2022
Goal-conditioned reinforcement learning is a crucial yet challenging algorithm which enables agents to achieve multiple user-specified goals when learning a set of skills in a dynamic environment. However, it typically requires millions of the environmental interactions explored by agents, which is sample-inefficient. In the paper, we propose a skill learning framework DR-GRL that aims to improve the sample efficiency and policy generalization by combining the Disentangled Representation learning and Goal-conditioned visual Reinforcement Learning. In a weakly supervised manner, we propose a Spatial Transform AutoEncoder (STAE) to learn an interpretable and controllable representation in which different parts correspond to different object attributes (shape, color, position). Due to the high controllability of the representations, STAE can simply recombine and recode the representations to generate unseen goals for agents to practice themselves. The manifold structure of the learned representation maintains consistency with the physical position, which is beneficial for reward calculation. We empirically demonstrate that DR-GRL significantly outperforms the previous methods in sample efficiency and policy generalization. In addition, DR-GRL is also easy to expand to the real robot.
* 8 pages; Accepted by RAL with ICRA 2022