Leveraging Interactive Semantic Alignment for Efficient Human-Object Interaction Detection with Vision-Language Models

核心概念

The proposed ISA-HOI method extensively leverages knowledge from the CLIP vision-language model to align interactive semantics between visual and textual features, leading to improved interaction feature representation and verb semantics for efficient HOI detection.

要約

The paper introduces a novel two-stage HOI detection method named ISA-HOI that leverages knowledge from the pre-trained CLIP vision-language model to improve the feature representation and alignment between interaction features and verb semantics.
Key highlights:

The IF module integrates global image features from CLIP, local object features, and predicted object labels' text embeddings to construct improved interaction queries.
The VSI module employs a lightweight transformer decoder to enhance the verb category labels' text embeddings by retrieving knowledge from image features, further aligning interaction features and verb semantics.
Extensive experiments on the HICO-DET and V-COCO datasets demonstrate the effectiveness of the proposed method, outperforming state-of-the-art approaches in both regular and zero-shot HOI detection settings.
The method exhibits superior training efficiency compared to other two-stage and one-stage HOI detection methods.

統計

The HICO-DET dataset contains 37,633 training images and 9,546 test images, with 600 HOI categories derived from 80 object categories and 117 action categories.
The V-COCO dataset contains 2,533 training images, 2,867 validation images, and 4,946 test images.
The evaluation metric used is mean Average Precision (mAP).

引用

"Our proposed IF module can integrate global and local features while narrowing the distance between interaction features and verb semantics."
"We further improve the verb category labels' text embeddings via the VSI module, reducing the heterogeneity between them and interaction features."

抽出されたキーインサイト

Exploring Interactive Semantic Alignment for Efficient HOI Detection with Vision-language Model

by Jihao Dong,R... 場所 arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12678.pdf

深掘り質問

How can the proposed method be extended to handle more complex interactions, such as those involving multiple humans or objects?

The proposed method, ISA-HOI, can be extended to handle more complex interactions involving multiple humans or objects by incorporating multi-instance learning techniques. Currently, the model focuses on detecting human-object interactions in a pairwise manner. To handle scenarios with multiple humans or objects, the model can be modified to consider all possible combinations of humans and objects within an image. This can be achieved by extending the interaction recognition module to process multiple instances simultaneously. By incorporating multi-instance learning, the model can capture interactions involving various combinations of humans and objects within an image, enabling it to detect complex interactions effectively.

What are the potential limitations of the CLIP-based approach, and how can they be addressed to further improve the performance of HOI detection?

While the CLIP-based approach offers significant advantages in aligning visual and textual features for HOI detection, it also has some limitations that can impact performance. One potential limitation is the reliance on pre-trained CLIP embeddings, which may not capture domain-specific nuances present in HOI detection tasks. To address this limitation, fine-tuning the CLIP model on HOI-specific data can help adapt the embeddings to better represent interactions between humans and objects. Additionally, the CLIP model may struggle with capturing fine-grained spatial information crucial for precise interaction detection. Incorporating spatial-aware features or positional embeddings can help enhance the model's ability to localize interactions accurately. Furthermore, the CLIP-based approach may face challenges in handling rare or unseen interactions due to limited training data. Augmenting the training data with diverse examples of rare interactions and employing techniques like data augmentation can help mitigate this limitation and improve the model's performance on rare or unseen interactions.

What other vision-language models or techniques could be explored to enhance the alignment between visual and textual features for HOI detection tasks?

In addition to CLIP, other vision-language models and techniques can be explored to enhance the alignment between visual and textual features for HOI detection tasks. One promising approach is to leverage models like VisualBERT or LXMERT, which are specifically designed for vision-language tasks and can effectively capture the relationship between visual and textual modalities. These models can be fine-tuned on HOI detection data to learn task-specific representations that facilitate accurate interaction recognition. Another technique worth exploring is cross-modal attention mechanisms, such as cross-modal transformers, which enable effective fusion of visual and textual information at different levels of abstraction. By incorporating these attention mechanisms into the model architecture, the alignment between visual and textual features can be further improved, leading to enhanced performance in HOI detection tasks.