We introduce ObSynth, an interactive system leveraging the domain knowledge embedded in large language models (LLMs) to help users design object models from high level natural language prompts. This is an example of specification reification, the process of taking a high-level, potentially vague specification and reifying it into a more concrete form. We evaluate ObSynth via a user study, leading to three key findings: first, object models designed using ObSynth are more detailed, showing that it often synthesizes fields users might have otherwise omitted. Second, a majority of objects, methods, and fields generated by ObSynth are kept by the user in the final object model, highlighting the quality of generated components. Third, ObSynth altered the workflow of participants: they focus on checking that synthesized components were correct rather than generating them from scratch, though ObSynth did not reduce the time participants took to generate object models.

We propose a new representation of visual data that disentangles object position from appearance. Our method, termed Deep Latent Particles (DLP), decomposes the visual input into low-dimensional latent ``particles'', where each particle is described by its spatial location and features of its surrounding region. To drive learning of such representations, we follow a VAE-based approach and introduce a prior for particle positions based on a spatial-softmax architecture, and a modification of the evidence lower bound loss inspired by the Chamfer distance between particles. We demonstrate that our DLP representations are useful for downstream tasks such as unsupervised keypoint (KP) detection, image manipulation, and video prediction for scenes composed of multiple dynamic objects. In addition, we show that our probabilistic interpretation of the problem naturally provides uncertainty estimates for particle locations, which can be used for model selection, among other tasks. Videos and code are available: https://taldatech.github.io/deep-latent-particles-web/

In nearly all cases, what is required is a small sample from a very large file (e.g. Therefore, an essential part of scraping is searching through an HTML document and finding the correct information. How that should be done is the matter of some debate, preferences, experience, and types of data. While all scraping and parsing methods are "correct", some of them have benefits that may be vital when more optimization is required. Some methods may be easier for specific types of data.

In human vision, the Principle of Common Fate of Gestalt Psychology (Wertheimer, 2012) has been shown to play an important role for object learning (Spelke, 1990). It posits that elements that are moving together tend to be perceived as one--a perceptual bias that may have evolved to be able to recognize camouflaged predators (Troscianko et al., 2009). In our work, we show that this principle can be successfully used also for machine vision by using it in a multi-stage object learning approach (Figure 1): First, we use unsupervised motion segmentation to obtain a candidate segmentation of a video frame. Second, we train generative object and background models on this segmentation. While the regions obtained by the motion segmentation are caused by objects moving in 3D, only visible parts can be segmented. To learn the actual objects (i.e., the causes), a crucial task for the object model is learning to generalize beyond the occlusions present in its input data. To measure success, we provide a dataset including object ground truth. As the last stage, we show that the learned object and background models can be combined into a flexible scene model that allows sampling manipulated novel scenes. Thus, in contrast to existing object-centric models trained end-to-end, our work aims at decomposing object learning into evaluable subproblems and testing the potential of exploiting object motions for building scalable object-centric models that allow for causally meaningful interventions in generation.

We introduce a framework for reasoning about what meaning is captured by the neurons in a trained neural network. We provide a strategy for discovering meaning by training a second model (referred to as an observer model) to classify the state of the model it observes (an object model) in relation to attributes of the underlying dataset. We implement and evaluate observer models in the context of a specific set of classification problems, employ heat maps for visualizing the relevance of components of an object model in the context of linear observer models, and use these visualizations to extract insights about the manner in which neural networks identify salient characteristics of their inputs. We identify important properties captured decisively in trained neural networks; some of these properties are denoted by individual neurons. Finally, we observe that the label proportion of a property denoted by a neuron is dependent on the depth of a neuron within a network; we analyze these dependencies, and provide an interpretation of them.

We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at bop.felk.cvut.cz.

We describe a novel method for learning templates for recognition and localization of objects drawn from categories. A generative model represents the configuration of multiple object parts with respect to an object coordinate system; these parts in turn generate image features. The complexity of the model in the number of features is low, meaning our model is much more efficient to train than comparative methods. Moreover, a variational approximation is introduced that allows learning to be orders of magnitude faster than previous approaches while incorporating many more features.

We describe a novel method for learning templates for recognition and localization of objects drawn from categories. A generative model represents the configuration of multiple object parts with respect to an object coordinate system; these parts in turn generate image features. The complexity of the model in the number of features is low, meaning our model is much more efficient to train than comparative methods. Moreover, a variational approximation is introduced that allows learning to be orders of magnitude faster than previous approaches while incorporating many more features.

Desirable Characteristics of a model include good representation of objects, fast and efficient learning algorithms that require as little supervised information as possible.