Scene graphs provide valuable information to many downstream tasks. Many scene graph generation (SGG) models solely use the limited annotated relation triples for training, leading to their underperformance on low-shot (few and zero) scenarios, especially on the rare predicates. To address this problem, we propose a novel semantic compositional learning strategy that makes it possible to construct additional, realistic relation triples with objects from different images. Specifically, our strategy decomposes a relation triple by identifying and removing the unessential component and composes a new relation triple by fusing with a semantically or visually similar object from a visual components dictionary, whilst ensuring the realisticity of the newly composed triple. Notably, our strategy is generic and can be combined with existing SGG models to significantly improve their performance. We performed a comprehensive evaluation on the benchmark dataset Visual Genome. For three recent SGG models, adding our strategy improves their performance by close to 50\%, and all of them substantially exceed the current state-of-the-art.
Designing complex 3D scenes has been a tedious, manual process requiring domain expertise. Emerging text-to-3D generative models show great promise for making this task more intuitive, but existing approaches are limited to object-level generation. We introduce \textbf{locally conditioned diffusion} as an approach to compositional scene diffusion, providing control over semantic parts using text prompts and bounding boxes while ensuring seamless transitions between these parts. We demonstrate a score distillation sampling--based text-to-3D synthesis pipeline that enables compositional 3D scene…
State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）；Google（谷歌）；The Chinese University of Hong Kong（香港中文大学）
Implicit neural rendering techniques have shown promising results for novel view synthesis. However, existing methods usually encode the entire scene as a whole, which is generally not aware of the object identity and limits the ability to the high-level editing tasks such as moving or adding furniture. In this paper, we present a novel neural scene rendering system, which learns an object-compositional neural radiance field and produces realistic rendering with editing capability for a clustered and real-world scene. Specifically, we design a novel two-pathway architecture, in which the scene…
Parts represent a basic unit of geometric and semantic similarity across different objects. We argue that part knowledge should be composable beyond the observed object classes. Towards this, we present 3D Compositional Zero-shot Learning as a problem of part generalization from seen to unseen object classes for semantic segmentation. We provide a structured study through benchmarking the task with the proposed Compositional-PartNet dataset. This dataset is created by processing the original PartNet to maximize part overlap across different objects. The existing point cloud part segmentation m…
Generating photorealistic images with controllable camera pose and scene contents is essential for many applications including AR/VR and simulation. Despite the fact that rapid progress has been made in 3D-aware generative models, most existing methods focus on object-centric images and are not applicable to generating urban scenes for free camera viewpoint control and scene editing. To address this challenging task, we propose UrbanGIRAFFE, which uses a coarse 3D panoptic prior, including the layout distribution of uncountable stuff and countable objects, to guide a 3D-aware generative model.…
University of Tübingen（图宾根大学）；MPI for Informatics（马克斯·普朗克信息学研究所）；TUM（慕尼黑工业大学）；Google（谷歌）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）
In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. old dog) of observed visual primitives states (e.g. old, cute) and objects (e.g. car, dog) in the training set. This is challenging because the same state can for example alter the visual appearance of a dog drastically differently from a car. As a solution, we propose a novel graph formulation called Compositional Graph Embedding (CGE) that learns image features, compositional classifiers, and latent representations of visual primitives in an end-to-end manner. The key to our approach is exploiting the dep…
SPOON: Towards Coherent Compositional 3D Scene Generation from Uncalibrated Multi-view Images
The University of Hong Kong（香港大学）；Shenzhen Loop Area Institute（深圳河套学院）；The Hong Kong University of Science and Technology（香港科技大学）；Sun Yat-sen University（中山大学）；TranscEngram
Synthesizing natural interactions between virtual humans and their 3D environments is critical for numerous applications, such as computer games and AR/VR experiences. Our goal is to synthesize humans interacting with a given 3D scene controlled by high-level semantic specifications as pairs of action categories and object instances, e.g., "sit on the chair". The key challenge of incorporating interaction semantics into the generation framework is to learn a joint representation that effectively captures heterogeneous information, including human body articulation, 3D object geometry, and the …
Information Technology University（信息科技大学）；University of Ontario Institute of Technology（安大略理工大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
NVIDIA Research（英伟达研究院）；Bar-Ilan University（巴伊兰大学）；Technion - Israel Institute of Technology（以色列理工学院）
People easily recognize new visual categories that are new combinations of known components. This compositional generalization capacity is critical for learning in real-world domains like vision and language because the long tail of new combinations dominates the distribution. Unfortunately, learning systems struggle with compositional generalization because they often build on features that are correlated with class labels even if they are not "essential" for the class. This leads to consistent misclassification of samples from a new distribution, like new combinations of known components. He…
Compositional zero-shot learning aims to recognize unseen compositions of seen visual primitives of object classes and their states. While all primitives (states and objects) are observable during training in some combination, their complex interaction makes this task especially hard. For example, wet changes the visual appearance of a dog very differently from a bicycle. Furthermore, we argue that relationships between compositions go beyond shared states or objects. A cluttered office can contain a busy table; even though these compositions don't share a state or object, the presence of a bu…
SceneTransporter: Optimal Transport-Guided Compositional Latent Diffusion for Single-Image Structured 3D Scene Generation
Xi’an Research Institute of Hi-Tech（西安高新技术研究院）；Tsinghua University（清华大学）；SkyWork AI；Tongji University（同济大学）；Fudan University（复旦大学）；Beijing Normal University（北京师范大学）
Johns Hopkins University（约翰斯·霍普金斯大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；University of Freiburg（弗莱堡大学）
Few-shot image classification consists of two consecutive learning processes: 1) In the meta-learning stage, the model acquires a knowledge base from a set of training classes. 2) During meta-testing, the acquired knowledge is used to recognize unseen classes from very few examples. Inspired by the compositional representation of objects in humans, we train a neural network architecture that explicitly represents objects as a dictionary of shared components and their spatial composition. In particular, during meta-learning, we train a knowledge base that consists of a dictionary of component r…
EVA: Mixture-of-Experts Semantic Variant Alignment for Compositional Zero-Shot Learning
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家级重点实验室）；National Engineering Research Center for Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究所）；Xi’an Jiaotong University（西安交通大学）
The appearance of the same object may vary in different scene images due to perspectives and occlusions between objects. Humans can easily identify the same object, even if occlusions exist, by completing the occluded parts based on its canonical image in the memory. Achieving this ability is still a challenge for machine learning, especially under the unsupervised learning setting. Inspired by such an ability of humans, this paper proposes a compositional scene modeling method to infer global representations of canonical images of objects without any supervision. The representation of each ob…
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人学院）
One of the key limitations of modern deep learning approaches lies in the amount of data required to train them. Humans, by contrast, can learn to recognize novel categories from just a few examples. Instrumental to this rapid learning ability is the compositional structure of concept representations in the human brain --- something that deep learning models are lacking. In this work, we make a step towards bridging this gap between human and machine learning by introducing a simple regularization technique that allows the learned representation to be decomposable into parts. Our method uses c…
Nanjing University of Science and Technology（南京理工大学）；Nanyang Technological University（南洋理工大学）
Point cloud segmentation is a fundamental visual understanding task in 3D vision. A fully supervised point cloud segmentation network often requires a large amount of data with point-wise annotations, which is expensive to obtain. In this work, we present the Compositional Prototype Network that can undertake point cloud segmentation with only a few labeled training data. Inspired by the few-shot learning literature in images, our network directly transfers label information from the limited training data to unlabeled test data for prediction. The network decomposes the representations of comp…
Zero-shot Compositional Action Recognition with Neural Logic Constraints
College of Computer Science and Technology（计算机科学与技术学院）；Zhejiang University（浙江大学）；AI Chip Center for Emerging Smart Systems（新兴智能系统人工智能芯片中心）；The Hong Kong University of Science and Technology（香港科学与技术大学）
University of New South Wales（新南威尔士大学）；Jiangnan University（江南大学）；University of Technology Sydney（悉尼科技大学）
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects. In OW-CZSL, methods built on the conventional closed-world setting degrade severely due to the unconstrained OW test space. While previous works alleviate the issue by pruning compositions according to external knowledge or correlations in seen pairs, they introduce biases that harm the generalization. Some methods thus predict state and object with independently constructed and trained classifiers, ignoring that attributes are highly context-dependent and visually entangled…
MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation
Academy for Engineering and Technology（工程与技术学院）；Cognition and Intelligent Technology Laboratory (CIT Lab)（认知与智能技术实验室）；School of Future Science and Engineering（未来科学与工程学院）；Jilin Provincial Key Laboratory of Intelligence Science and Engineering（吉林省智能科学与工程重点实验室）；Engineering Research Center of AI and Robotics, Ministry of Education（人工智能与机器人工程研究中心）
Beijing University of Posts and Telecommunications（北京邮电大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image and a composition into a common embedding space to measure their compatibility score. However, both attributes and objects share the visual representations learned above, leading the model to exploit spurious correlations and bias towards seen pairs. Instead, we reconsider CZSL as an out-of-distribution generalization problem. If an object is treated as a domain, we can learn object-invariant fea…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Tübingen（蒂宾根大学）
Deep generative models allow for photorealistic image synthesis at high resolutions. But for many applications, this is not enough: content creation also needs to be controllable. While several recent works investigate how to disentangle underlying factors of variation in the data, most of them operate in 2D and hence ignore that our world is three-dimensional. Further, only few works consider the compositional nature of scenes. Our key hypothesis is that incorporating a compositional 3D scene representation into the generative model leads to more controllable image synthesis. Representing sce…
University of Tübingen（蒂宾根大学）；ETH Zurich（苏黎世联邦理工学院）；Max Planck Institute (MPI) for Informatics（马克斯·普朗克信息学研究所）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）
Compositional Zero-Shot learning (CZSL) aims to recognize unseen compositions of state and object visual primitives seen during training. A problem with standard CZSL is the assumption of knowing which unseen compositions will be available at test time. In this work, we overcome this assumption operating on the open world setting, where no limit is imposed on the compositional space at test time, and the search space contains a large number of unseen compositions. To address this problem, we propose a new approach, Compositional Cosine Graph Embeddings (Co-CGE), based on two principles. First,…
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation
The Future Network of Intelligence Institute, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）未来智能网络研究院）；School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理工学院）
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Florida State University（佛罗里达州立大学）
Generalized compositional zero-shot learning means to learn composed concepts of attribute-object pairs in a zero-shot fashion, where a model is trained on a set of seen concepts and tested on a combined set of seen and unseen concepts. This task is very challenging because of not only the gap between seen and unseen concepts but also the contextual dependency between attributes and objects. This paper introduces a new approach, termed translational concept embedding, to solve these two difficulties in a unified framework. It models the effect of applying an attribute to an object as adding a …
With the expressed goal of improving system transparency and visual grounding in the reasoning process in VQA, we present a modular system for the task of compositional VQA based on scene graphs. Our system is called "Adventurer's Treasure Hunt" (or ATH), named after an analogy we draw between our model's search procedure for an answer and an adventurer's search for treasure. We developed ATH with three characteristic features in mind: 1. By design, ATH allows us to explicitly quantify the impact of each of the sub-components on overall VQA performance, as well as their performance on their in…
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions formed from seen state and object during training. Since the same state may be various in the visual appearance while entangled with different objects, CZSL is still a challenging task. Some methods recognize state and object with two trained classifiers, ignoring the impact of the interaction between object and state; the other methods try to learn the joint representation of the state-object compositions, leading to the domain gap between seen and unseen composition sets. In this paper, we propose a novel Siamese C…
Khoury College of Computer Sciences, Northeastern University（东北大学库理计算机学院）
We develop a novel compositional generative model for zero- and few-shot learning to recognize fine-grained classes with a few or no training samples. Our key observation is that generating holistic features for fine-grained classes fails to capture small attribute differences between classes. Therefore, we propose a feature composition framework that learns to extract attribute features from training samples and combines them to construct fine-grained features for rare and unseen classes. Feature composition allows us to not only selectively compose features of every class from only relevant …
University of Virginia（弗吉尼亚大学）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）
In this paper, we propose Text2Scene, a model that generates various forms of compositional scene representations from natural language descriptions. Unlike recent works, our method does NOT use Generative Adversarial Networks (GANs). Text2Scene instead learns to sequentially generate objects and their attributes (location, size, appearance, etc) at every time step by attending to different parts of the input text and the current status of the generated scene. We show that under minor modifications, the proposed framework can handle the generation of different forms of scene representations, i…
Michigan State University（密歇根州立大学）；University of Michigan（密歇根大学）
In this paper, we study the problem of recognizing compositional attribute-object concepts within the zero-shot learning (ZSL) framework. We propose an episode-based cross-attention (EpiCA) network which combines merits of cross-attention mechanism and episode-based training strategy to recognize novel compositional concepts. Firstly, EpiCA bases on cross-attention to correlate concept-visual information and utilizes the gated pooling layer to build contextualized representations for both images and concepts. The updated representations are used for a more in-depth multi-modal relevance calcul…
Progressive Reasoning with Primitive Correction for Compositional Zero-Shot Learning
School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Key Laboratory of Big Data & Artificial Intelligence in Transportation (Ministry of Education)（交通运输大数据与人工智能教育部重点实验室）
Visual concepts (e.g., red apple, big elephant) are often semantically compositional and each element of the compositions can be reused to construct novel concepts (e.g., red elephant). Compositional feature synthesis, which generates image feature distributions exploiting the semantic compositionality, is a promising approach to sample-efficient model generalization. In this work, we propose a task-aware feature generation (TFG) framework for compositional learning, which generates features of novel visual concepts by transferring knowledge from previously seen concepts. These synthetic featu…
Monash University（莫纳什大学）；The Chinese University of Hong Kong（香港中文大学）；University of Oxford（牛津大学）；Nanyang Technological University（南洋理工大学）
The neural implicit representation has shown its effectiveness in novel view synthesis and high-quality 3D reconstruction from multi-view images. However, most approaches focus on holistic scene representation yet ignore individual objects inside it, thus limiting potential downstream applications. In order to learn object-compositional representation, a few works incorporate the 2D semantic map as a cue in training to grasp the difference between objects. But they neglect the strong connections between object geometry and instance semantic information, which leads to inaccurate modeling of in…
