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"abstract": "Affordance detection, which refers to perceiving objects with potential action possibilities in images, is a challenging task since the possible affordance depends on the person's purpose in real-world application scenarios. The existing works mainly extract the inherent human-object dependencies from image/video to accommodate affordance properties that change dynamically. In this paper, we explore to perceive affordances from a vision-language perspective, and consider the challenging phrase-based affordance detection task, i.e., given a set of phrases describing the potential actions, all the object regions in a scene with the same affordance should be detected. To this end, we propose a <bold>C</bold>yclic <bold>B</bold>ilateral <bold>C</bold>onsistency <bold>E</bold>nhancement Network (CBCE-Net) to align language and vision features in a progressive manner. Specifically, the presented CBCE-Net consists of a mutual guided vision-language module that updates the common features of vision and language in a progressive manner, and a <bold>C</bold>yclic <bold>I</bold>nteraction <bold>M</bold>odule (CIM) that facilitates the perception of possible interaction with objects in a cyclic manner. In addition, we extend the public Purpose-driven Affordance Dataset (PAD) by annotating affordance categories with short phrases. The extensive contrastive experimental results demonstrate the superior performance of our method over nine typical methods from four relevant fields in terms of both objective metrics and visual quality. The related code and dataset will be released at <uri>https://github.com/lulsheng/CBCE-Net</uri>. <italic>Impact Statement</italic>—Affordance learning is concerned with the possible set of actions that an environment can offer to an actor. At present, almost all work focus on utilizing cues of human-object interactions in visual media such as images and videos to learn affordances. Few work explores affordance using language information, which is crucial to building more intelligent agents because we live a multimodal world. In this paper, we propose a phrase-based affordance detection task combining vision and language modalities. We first build a vision-language dataset based on the public Purpose-driven Affordance Dataset (PAD). Then we propose a method to fuse and align multi-modal features. Our method outperforms relevant methods from several other fields in terms of both objective metrics and visual quality. The proposed dataset and method can facilitate building more intelligent agents that can better comprehend the surrounding environment. For instance, the phase-based affordance detection can be used in building more intelligent home service systems. The agents can understand instructions of instructions without specific entities.",
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"content": "Affordance detection, which refers to perceiving objects with potential action possibilities in images, is a challenging task since the possible affordance depends on the person's purpose in real-world application scenarios. The existing works mainly extract the inherent human-object dependencies from image/video to accommodate affordance properties that change dynamically. In this paper, we explore to perceive affordances from a vision-language perspective, and consider the challenging phrase-based affordance detection task, i.e., given a set of phrases describing the potential actions, all the object regions in a scene with the same affordance should be detected. To this end, we propose a <bold>C</bold>yclic <bold>B</bold>ilateral <bold>C</bold>onsistency <bold>E</bold>nhancement Network (CBCE-Net) to align language and vision features in a progressive manner. Specifically, the presented CBCE-Net consists of a mutual guided vision-language module that updates the common features of vision and language in a progressive manner, and a <bold>C</bold>yclic <bold>I</bold>nteraction <bold>M</bold>odule (CIM) that facilitates the perception of possible interaction with objects in a cyclic manner. In addition, we extend the public Purpose-driven Affordance Dataset (PAD) by annotating affordance categories with short phrases. The extensive contrastive experimental results demonstrate the superior performance of our method over nine typical methods from four relevant fields in terms of both objective metrics and visual quality. The related code and dataset will be released at <uri>https://github.com/lulsheng/CBCE-Net</uri>. <italic>Impact Statement</italic>—Affordance learning is concerned with the possible set of actions that an environment can offer to an actor. At present, almost all work focus on utilizing cues of human-object interactions in visual media such as images and videos to learn affordances. Few work explores affordance using language information, which is crucial to building more intelligent agents because we live a multimodal world. In this paper, we propose a phrase-based affordance detection task combining vision and language modalities. We first build a vision-language dataset based on the public Purpose-driven Affordance Dataset (PAD). Then we propose a method to fuse and align multi-modal features. Our method outperforms relevant methods from several other fields in terms of both objective metrics and visual quality. The proposed dataset and method can facilitate building more intelligent agents that can better comprehend the surrounding environment. For instance, the phase-based affordance detection can be used in building more intelligent home service systems. The agents can understand instructions of instructions without specific entities.",
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"normalizedAbstract": "Affordance detection, which refers to perceiving objects with potential action possibilities in images, is a challenging task since the possible affordance depends on the person's purpose in real-world application scenarios. The existing works mainly extract the inherent human-object dependencies from image/video to accommodate affordance properties that change dynamically. In this paper, we explore to perceive affordances from a vision-language perspective, and consider the challenging phrase-based affordance detection task, i.e., given a set of phrases describing the potential actions, all the object regions in a scene with the same affordance should be detected. To this end, we propose a Cyclic Bilateral Consistency Enhancement Network (CBCE-Net) to align language and vision features in a progressive manner. Specifically, the presented CBCE-Net consists of a mutual guided vision-language module that updates the common features of vision and language in a progressive manner, and a Cyclic Interaction Module (CIM) that facilitates the perception of possible interaction with objects in a cyclic manner. In addition, we extend the public Purpose-driven Affordance Dataset (PAD) by annotating affordance categories with short phrases. The extensive contrastive experimental results demonstrate the superior performance of our method over nine typical methods from four relevant fields in terms of both objective metrics and visual quality. The related code and dataset will be released at https://github.com/lulsheng/CBCE-Net. Impact Statement—Affordance learning is concerned with the possible set of actions that an environment can offer to an actor. At present, almost all work focus on utilizing cues of human-object interactions in visual media such as images and videos to learn affordances. Few work explores affordance using language information, which is crucial to building more intelligent agents because we live a multimodal world. In this paper, we propose a phrase-based affordance detection task combining vision and language modalities. We first build a vision-language dataset based on the public Purpose-driven Affordance Dataset (PAD). Then we propose a method to fuse and align multi-modal features. Our method outperforms relevant methods from several other fields in terms of both objective metrics and visual quality. The proposed dataset and method can facilitate building more intelligent agents that can better comprehend the surrounding environment. For instance, the phase-based affordance detection can be used in building more intelligent home service systems. The agents can understand instructions of instructions without specific entities.",
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"abstract": "It has been shown that it is possible to generate perceptual illusions of ownership in immersive virtual reality (IVR) over a virtual body seen from first person perspective, in other words over a body that visually substitutes the person's real body. This can occur even when the virtual body is quite different in appearance from the person's real body. However, investigation of the psychological, behavioral and attitudinal consequences of such body transformations remains an interesting problem with much to be discovered. Thirty six Caucasian people participated in a between-groups experiment where they played a West-African Djembe hand drum while immersed in IVR and with a virtual body that substituted their own. The virtual hand drum was registered with a physical drum. They were alongside a virtual character that played a drum in a supporting, accompanying role. In a baseline condition participants were represented only by plainly shaded white hands, so that they were able merely to play. In the experimental condition they were represented either by a casually dressed dark-skinned virtual body (Casual Dark-Skinned - CD) or by a formal suited light-skinned body (Formal Light-Skinned - FL). Although participants of both groups experienced a strong body ownership illusion towards the virtual body, only those with the CD representation showed significant increases in their movement patterns for drumming compared to the baseline condition and compared with those embodied in the FL body. Moreover, the stronger the illusion of body ownership in the CD condition, the greater this behavioral change. A path analysis showed that the observed behavioral changes were a function of the strength of the illusion of body ownership towards the virtual body and its perceived appropriateness for the drumming task. These results demonstrate that full body ownership illusions can lead to substantial behavioral and possibly cognitive changes depending on the appearance of the virtual body. This could be important for many applications such as learning, education, training, psychotherapy and rehabilitation using IVR.",
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"content": "It has been shown that it is possible to generate perceptual illusions of ownership in immersive virtual reality (IVR) over a virtual body seen from first person perspective, in other words over a body that visually substitutes the person's real body. This can occur even when the virtual body is quite different in appearance from the person's real body. However, investigation of the psychological, behavioral and attitudinal consequences of such body transformations remains an interesting problem with much to be discovered. Thirty six Caucasian people participated in a between-groups experiment where they played a West-African Djembe hand drum while immersed in IVR and with a virtual body that substituted their own. The virtual hand drum was registered with a physical drum. They were alongside a virtual character that played a drum in a supporting, accompanying role. In a baseline condition participants were represented only by plainly shaded white hands, so that they were able merely to play. In the experimental condition they were represented either by a casually dressed dark-skinned virtual body (Casual Dark-Skinned - CD) or by a formal suited light-skinned body (Formal Light-Skinned - FL). Although participants of both groups experienced a strong body ownership illusion towards the virtual body, only those with the CD representation showed significant increases in their movement patterns for drumming compared to the baseline condition and compared with those embodied in the FL body. Moreover, the stronger the illusion of body ownership in the CD condition, the greater this behavioral change. A path analysis showed that the observed behavioral changes were a function of the strength of the illusion of body ownership towards the virtual body and its perceived appropriateness for the drumming task. These results demonstrate that full body ownership illusions can lead to substantial behavioral and possibly cognitive changes depending on the appearance of the virtual body. This could be important for many applications such as learning, education, training, psychotherapy and rehabilitation using IVR.",
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"content": "The structural properties of acyclic database schemas, especially the interrelationships between acyclic join dependencies and multivalued dependencies, are examined in this paper. The intersection closure of a database schema is a semilattice, which is usually represented by Hasse diagram in algebra. The Hasse diagram of a schema is analogous to the Bachman diagram of a network model. Based on the topological structures of Hasse diagrams, the acyclic join dependency is proved to be equivalent to a set of conflict-free multivalued dependencies. Furthermore, we show that if the Hasse diagram is loop free, then the corresponding set of multivalued dependencies is also contention-free. This result is consistent with previous results derived from the Bachman diagram.",
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