Open-vocabulary scene graph generation (OVSGG) extends traditional SGG by recognizing novel objects and relationships beyond predefined categories, leveraging the knowledge from pre-trained large-scale models. Existing OVSGG methods always adopt a two-stage pipeline: 1) Infusing knowledge into large-scale models via pre-training on large datasets; 2) Transferring knowledge from pre-trained models with fully annotated scene graphs during supervised fine-tuning. However, due to a lack of explicit interaction modeling, these methods struggle to distinguish between interacting and non-interacting instances of the same object category. This limitation induces critical issues in both stages of OVSGG: it generates noisy pseudo-supervision from mismatched objects during knowledge infusion, and causes ambiguous query matching during knowledge transfer. To this end, in this paper, we propose an interACtion-Centric end-to-end OVSGG framework (ACC) in an interaction-driven paradigm to minimize these mismatches. For interactioncentric knowledge infusion, ACC employs a bidirectional interaction prompt for robust pseudo-supervision generation to enhance the model's interaction knowledge. For interaction-centric knowledge transfer, ACC first adopts interaction-guided query selection that prioritizes pairing interacting objects to reduce interference from non-interacting ones. Then, it integrates interaction-consistent knowledge distillation to bolster robustness by pushing relational foreground away from the background while retaining general knowledge. Extensive experimental results on three benchmarks show that ACC achieves state-of-the-art performance, demonstrating the potential of interaction-centric paradigms for real-world applications.

Based on the formulation of the likelihood function pΘ(G|I) = fΘ(G,I)/ZΘ(I), we can reformulate the gradient of log-likelihood function as: ΘL(Θ) = EG pd[ Θ log fΘ(G,I)] Θ log ZΘ(I). Theorem 2. In the initialization phase, the potential function ψtriplet(r,yoh,yot) for modeling label dependency is omitted in p(G|I), yielding a simplified model distribution ˆp(G|I). Now, we can exactly derive that q(G) = ˆp(G|I). Theorem 3. In the update phase, we use the full expression of p(G|I) with the potential function ψtriplet(r,yoh,yot) for modeling label dependency. In this case, maximizing L(q) is equivalent to minimizing the KL divergence term, and the minimum occurs when q(yo) = p(yo,I).

Objects and their relationships are critical contents for image understanding. A scene graph provides a structured description that captures these properties of an image. However, reasoning about the relationships between objects is very challenging and only a few recent works have attempted to solve the problem of generating a scene graph from an image. In this paper, we present a novel method that improves scene graph generation by explicitly modeling inter-dependency among the entire object instances. We design a simple and effective relational embedding module that enables our model to jointly represent connections among all related objects, rather than focus on an object in isolation. Our novel method significantly benefits two main parts of the scene graph generation task: object classification and relationship classification. Using it on top of a basic Faster R-CNN, our model achieves state-of-the-art results on the Visual Genome benchmark.

Traditional 3D scene graph methods may recognize the static elements of this scene, such as identifying a booth situated on the ground. However, a more ideal, advanced, and dynamic perception is required for real-world scenarios.

In this paper, we introduce the new Aero-Eye dataset that focuses on multi-object relationship modeling in aerial videos.

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To address this, recent research attempts to train single-stage models that arecomputationally efficient.