There are many applications that incorporating a human appearance and intending to simulate dialog, but in most of the cases knowledge conversational bot is stored database created by experts. However, very few researches have investigated idea creating chat-bot with an artificial character personality starting from web pages or plain text about certain person. This paper describes approach identifying important facts texts describing life (including personality) historical figure for...

We address an essential problem in computer vision, that of unsupervised foreground object segmentation video, where a main interest video sequence should be automatically separated from its background. An efficient solution to this task would enable large-scale interpretation at high semantic level the absence costly manual labeling. propose method for generating soft masks based on automatic selection and learning highly probable positive features. show such features can selected...

Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing suitable scenario for studying generalization properties ML models. The existing are focused supervised learning, and best our knowledge, there none unsupervised learning. Therefore, we introduce an anomaly detection benchmark with shifts over time, built Kyoto-2006+, traffic dataset network intrusion detection. This type meets...

We present a method for learning multiple scene representations given small labeled set, by exploiting the relationships between such in form of multi-task hypergraph. also show how we can use hypergraph to improve powerful pretrained VisTrans-former model without any additional data. In our hypergraph, each node is an interpretation layer (e.g., depth or segmentation) scene. Within hyperedge, one several input nodes predict at output node. Thus, could be some hyperedges and others. this...

We propose a dual system for unsupervised object segmentation in video, which brings together two modules with complementary properties: space-time graph that discovers objects videos and deep network learns powerful features. The uses an iterative knowledge exchange policy. A novel spectral clustering process on the produces masks passed to as pseudo-labels. net segment single frames what video passes back strong image-level features improve its node-level next iteration. Knowledge is...

We address the challenging task of foreground object discovery and segmentation in video. introduce an efficient solution, suitable for both unsupervised supervised scenarios, based on a spacetime graph representation video sequence. ensure fine grained with one-to-one correspondences between nodes pixels. formulate as spectral clustering problem by exploiting spatio-temporal consistency scene elements terms motion appearance. Graph that belong to main interest should form strong cluster,...

The human ability to synchronize the feedback from all their senses inspired recent works in multi-task and multi-modal learning. While these rely on expensive supervision, our graph requires only pseudo-labels expert models. Every node represents a task, each edge learns between tasks transformations. Once initialized, self-supervised, based novel consensus shift algorithm that intelligently exploits agreement pathways generate new for next learning cycle. We demonstrate significant...

We introduce a formalization and benchmark for the unsupervised anomaly detection task in distribution-shift scenario. Our work builds upon iWildCam dataset, and, to best of our knowledge, we are first propose such an approach visual data. empirically validate that environment-aware methods perform better cases when compared with basic Empirical Risk Minimization (ERM). next extension generating positive samples contrastive considers environment labels training, improving ERM baseline score by 8.7%.

We present a method for learning multiple scene representations given small labeled set, by exploiting the relationships between such in form of multi-task hypergraph. also show how we can use hypergraph to improve powerful pretrained VisTransformer model without any additional data. In our hypergraph, each node is an interpretation layer (e.g., depth or segmentation) scene. Within hyperedge, one several input nodes predict at output node. Thus, could be some hyperedges and others. this way,...

We tackle the problem of robust novelty detection, where we aim to detect novelties in terms semantic content while being invariant changes other, irrelevant factors. Specifically, operate a setup with multiple environments, determine set features that are associated more rather than relevant for task. Thus, propose method starts pretrained embedding and multi-env manages rank based on their environment-focus. First, compute per-feature score feature distribution variance between envs. Next,...

Novelty detection aims at finding samples that differ in some form from the distribution of seen samples. But not all changes are created equal. Data can suffer a multitude shifts, and we might want to detect only types relevant changes. Similar works out-of-distribution generalization, propose use formalization separating into semantic or content changes, our task, style irrelevant. Within this formalization, define robust novelty as task while being distributional shifts. Leveraging...

We address an essential problem in computer vision, that of unsupervised object segmentation video, where a main interest video sequence should be automatically separated from its background. An efficient solution to this task would enable large-scale interpretation at high semantic level the absence costly manually labeled ground truth. propose method for generating foreground soft-segmentation masks based on automatic selection and learning highly probable positive features. show such...

We propose a dual system for unsupervised object segmentation in video, which brings together two modules with complementary properties: space-time graph that discovers objects videos and deep network learns powerful features. The uses an iterative knowledge exchange policy. A novel spectral clustering process on the produces masks passed to as pseudo-labels. net segment single frames what video passes back strong image-level features improve its node-level next iteration. Knowledge is...