Our goal is to generate a policy to complete an unseen task given just a single video demonstration of the task in a given domain. We hypothesize that to successfully generalize to unseen complex tasks from a single video demonstration, it is necessary to explicitly incorporate the compositional structure of the tasks into the model. To this end, we propose Neural Task Graph (NTG) Networks, which use conjugate task graph as the intermediate representation to modularize both the video demonstration and the derived policy. We empirically show NTG achieves inter-task generalization on two complex tasks: Block Stacking in BulletPhysics and Object Collection in AI2-THOR. NTG improves data efficiency with visual input as well as achieve strong generalization without the need for dense hierarchical supervision. We further show that similar performance trends hold when applied to real-world data. We show that NTG can effectively predict task structure on the JIGSAWS surgical dataset and generalize to unseen tasks.
Few-shot learning aims to classify unseen classes with a few training examples. While recent works have shown that standard mini-batch training with a carefully designed training strategy can improve generalization ability for unseen classes, well-known problems in deep networks such as memorizing training statistics have been less explored for few-shot learning. To tackle this issue, we propose self-augmentation that consolidates self-mix and self-distillation. Specifically, we exploit a regional dropout technique called self-mix, in which a patch of an image is substituted into other values …
Microsoft Research Asia（微软亚洲研究院）；School of Data Science, Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Institute for Data Science, Tsinghua University（清华大学数据科学研究院）
Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unseen distributions. Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increasing interests in recent years. Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain. Great progress has been made in the area of domain generalization for years. This p…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；University of Tübingen（图宾根大学）
Adversarial training yields robust models against a specific threat model, e.g., $L_\infty$ adversarial examples. Typically robustness does not generalize to previously unseen threat models, e.g., other $L_p$ norms, or larger perturbations. Our confidence-calibrated adversarial training (CCAT) tackles this problem by biasing the model towards low confidence predictions on adversarial examples. By allowing to reject examples with low confidence, robustness generalizes beyond the threat model employed during training. CCAT, trained only on $L_\infty$ adversarial examples, increases robustness ag…
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University（清华大学-伯克利深圳学院，清华大学）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；School of Industrial and Systems and Engineering, Georgia Institute of Technology（佐治亚理工学院工业与系统工程学院）
Istituto Italiano di Tecnologia（意大利技术研究院）；Stanford University（斯坦福大学）；Intel Labs（英特尔实验室）；Università di Verona（维罗纳大学）
We are concerned with learning models that generalize well to different \emph{unseen} domains. We consider a worst-case formulation over data distributions that are near the source domain in the feature space. Only using training data from a single source distribution, we propose an iterative procedure that augments the dataset with examples from a fictitious target domain that is "hard" under the current model. We show that our iterative scheme is an adaptive data augmentation method where we append adversarial examples at each iteration. For softmax losses, we show that our method is a data-…
Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）；Indian Institute of Science, Bangalore（印度科学理工学院班加罗尔分校）
In this work, for the first time, we address the problem of universal cross-domain retrieval, where the test data can belong to classes or domains which are unseen during training. Due to dynamically increasing number of categories and practical constraint of training on every possible domain, which requires large amounts of data, generalizing to both unseen classes and domains is important. Towards that goal, we propose SnMpNet (Semantic Neighbourhood and Mixture Prediction Network), which incorporates two novel losses to account for the unseen classes and domains encountered during testing. …
The Chinese University of Hong Kong（香港中文大学）；Chinese Academy of Sciences（中国科学院）
Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tackles the challenging problem of domain generalization, i.e., learning a model from multi-domain source data such that it can directly generalize to an unseen target domain. We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Our learning scheme roots in the gradient-based meta-learning, by explicitly simulating domain shift with virtual meta-train and m…
University of California, San Diego（加州大学圣地亚哥分校）；University of Hong Kong（香港大学）；AWS AI, Amazon（亚马逊AWS人工智能）
Recent progress on vision-language foundation models have brought significant advancement to building general-purpose robots. By using the pre-trained models to encode the scene and instructions as inputs for decision making, the instruction-conditioned policy can generalize across different objects and tasks. While this is encouraging, the policy still fails in most cases given an unseen task or environment. In this work, we propose Policy Adaptation from Foundation model Feedback (PAFF). When deploying the trained policy to a new task or a new environment, we first let the policy play with r…
Huazhong University of Science and Technology（华中科技大学）；Tsinghua University（清华大学）；MEGVII Technology（旷视科技）
Although existing multi-object tracking (MOT) algorithms have obtained competitive performance on various benchmarks, almost all of them train and validate models on the same domain. The domain generalization problem of MOT is hardly studied. To bridge this gap, we first draw the observation that the high-level information contained in natural language is domain invariant to different tracking domains. Based on this observation, we propose to introduce natural language representation into visual MOT models for boosting the domain generalization ability. However, it is infeasible to label every…
Hyper-Decision Transformer for Efficient Online Policy Adaptation
Carnegie Mellon University（卡内基梅隆大学）；University of Montreal（蒙特利尔大学）；Mila（米拉人工智能研究所）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；UMass Amherst（马萨诸塞大学阿默斯特分校）
Generalizing Surgical Instruments Segmentation to Unseen Domains with One-to-Many Synthesis
Shun Hing Institute of Advanced Engineering（顺兴高等研究院）；CUHK（香港中文大学）；Wellcome / EPSRC Centre for Interventional and Surgical Sciences（惠康/英国工程与自然科学研究委员会介入与外科科学中心）；UCL（伦敦大学学院）；NUS（新加坡国立大学）
Despite learning-based visual odometry (VO) has shown impressive results in recent years, the pretrained networks may easily collapse in unseen environments. The large domain gap between training and testing data makes them difficult to generalize to new scenes. In this paper, we propose an online adaptation framework for deep VO with the assistance of scene-agnostic geometric computations and Bayesian inference. In contrast to learning-based pose estimation, our method solves pose from optical flow and depth while the single-view depth estimation is continuously improved with new observations…
PointDGRWKV: Generalizing RWKV-like Architecture to Unseen Domains for Point Cloud Classification
Shanghai Jiao Tong University（上海交通大学）；The University of Tokyo（东京大学）；Nanjing University（南京大学）；Nanyang Technological University（南洋理工大学）；The University of Western Australia（西澳大学）；National University of Singapore（新加坡国立大学）
Vision and language navigation (VLN) is a challenging visually-grounded language understanding task. Given a natural language navigation instruction, a visual agent interacts with a graph-based environment equipped with panorama images and tries to follow the described route. Most prior work has been conducted in indoor scenarios where best results were obtained for navigation on routes that are similar to the training routes, with sharp drops in performance when testing on unseen environments. We focus on VLN in outdoor scenarios and find that in contrast to indoor VLN, most of the gain in ou…
University of Washington（华盛顿大学）；Nvidia（英伟达）；Massachusetts Institute of Technology（麻省理工学院）
Teaching a multi-fingered dexterous robot to grasp objects in the real world has been a challenging problem due to its high dimensional state and action space. We propose a robot-learning system that can take a small number of human demonstrations and learn to grasp unseen object poses given partially occluded observations. Our system leverages a small motion capture dataset and generates a large dataset with diverse and successful trajectories for a multi-fingered robot gripper. By adding domain randomization, we show that our dataset provides robust grasping trajectories that can be transfer…
Video In-context Learning: Autoregressive Transformers are Zero-Shot Video Imitators
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；Microsoft Research Asia（微软亚洲研究院）
Generalizable object manipulation skills are critical for intelligent and multi-functional robots to work in real-world complex scenes. Despite the recent progress in reinforcement learning, it is still very challenging to learn a generalizable manipulation policy that can handle a category of geometrically diverse articulated objects. In this work, we tackle this category-level object manipulation policy learning problem via imitation learning in a task-agnostic manner, where we assume no handcrafted dense rewards but only a terminal reward. Given this novel and challenging generalizable poli…
