Zero-Shot Human-Object Interaction Recognition via Affordance Graphs

• URL: http://arxiv.org/abs/2009.01039v1
• Date: Wed, 2 Sep 2020 13:14:44 GMT
• Title: Zero-Shot Human-Object Interaction Recognition via Affordance Graphs
• Authors: Alessio Sarullo, Tingting Mu
• Abstract summary: We propose a new approach for Zero-Shot Human-Object Interaction Recognition. Our approach makes use of knowledge external to the image content in the form of a graph. We evaluate our approach on several datasets and show that it outperforms the current state of the art.
• Score: 3.867143522757309
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new approach for Zero-Shot Human-Object Interaction Recognition in the challenging setting that involves interactions with unseen actions (as opposed to just unseen combinations of seen actions and objects). Our approach makes use of knowledge external to the image content in the form of a graph that models affordance relations between actions and objects, i.e., whether an action can be performed on the given object or not. We propose a loss function with the aim of distilling the knowledge contained in the graph into the model, while also using the graph to regularise learnt representations by imposing a local structure on the latent space. We evaluate our approach on several datasets (including the popular HICO and HICO-DET) and show that it outperforms the current state of the art.

Related papers

• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• Knowledge Guided Bidirectional Attention Network for Human-Object Interaction Detection [3.0915392100355192]
  We argue that the independent use of the bottom-up parsing strategy in HOI is counter-intuitive and could lead to the diffusion of attention. We introduce a novel knowledge-guided top-down attention into HOI, and propose to model the relation parsing as a "look and search" process. We implement the process via unifying the bottom-up and top-down attention in a single encoder-decoder based model.
  arXiv  Detail & Related papers  (2022-07-16T16:42:49Z)
• Skeleton-Based Mutually Assisted Interacted Object Localization and Human Action Recognition [111.87412719773889]
  We propose a joint learning framework for "interacted object localization" and "human action recognition" based on skeleton data. Our method achieves the best or competitive performance with the state-of-the-art methods for human action recognition.
  arXiv  Detail & Related papers  (2021-10-28T10:09:34Z)
• Exploiting Scene Graphs for Human-Object Interaction Detection [81.49184987430333]
  Human-Object Interaction (HOI) detection is a fundamental visual task aiming at localizing and recognizing interactions between humans and objects. We propose a novel method to exploit this information, through the scene graph, for the Human-Object Interaction (SG2HOI) detection task. Our method, SG2HOI, incorporates the SG information in two ways: (1) we embed a scene graph into a global context clue, serving as the scene-specific environmental context; and (2) we build a relation-aware message-passing module to gather relationships from objects' neighborhood and transfer them into interactions.
  arXiv  Detail & Related papers  (2021-08-19T09:40:50Z)
• Mutual Graph Learning for Camouflaged Object Detection [31.422775969808434]
  A major challenge is that intrinsic similarities between foreground objects and background surroundings make the features extracted by deep model indistinguishable. We design a novel Mutual Graph Learning model, which generalizes the idea of conventional mutual learning from regular grids to the graph domain. In contrast to most mutual learning approaches that use a shared function to model all between-task interactions, MGL is equipped with typed functions for handling different complementary relations.
  arXiv  Detail & Related papers  (2021-04-03T10:14:39Z)
• DRG: Dual Relation Graph for Human-Object Interaction Detection [65.50707710054141]
  We tackle the challenging problem of human-object interaction (HOI) detection. Existing methods either recognize the interaction of each human-object pair in isolation or perform joint inference based on complex appearance-based features. In this paper, we leverage an abstract spatial-semantic representation to describe each human-object pair and aggregate the contextual information of the scene via a dual relation graph.
  arXiv  Detail & Related papers  (2020-08-26T17:59:40Z)
• ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection [101.56529337489417]
  We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of human, action, object> in images. We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs. Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities.
  arXiv  Detail & Related papers  (2020-08-14T09:11:18Z)
• A Graph-based Interactive Reasoning for Human-Object Interaction Detection [71.50535113279551]
  We present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs. We construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Our framework is end-to-end trainable and free from costly annotations like human pose.
  arXiv  Detail & Related papers  (2020-07-14T09:29:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.