Rethinking Human-Object Interaction Evaluation for both Vision-Language Models and HOI-Specific Methods

Prior human-object interaction (HOI) detection methods have integrated early vision-language models (VLMs) such as CLIP, but only as supporting components within their frameworks. In contrast, recent advances in large, generative VLMs suggest that these models may already possess strong ability to understand images involving HOI. This naturally raises an important question: can general-purpose standalone VLMs effectively solve HOI detection, and how do they compare with specialized HOI methods? Answering this requires a benchmark that can accommodate both paradigms. However, existing HOI benchmarks such as HICO-DET were developed before the emergence of modern VLMs, and their evaluation protocols require exact matches to annotated HOI classes. This is poorly aligned with the generative nature of VLMs, which often yield multiple valid interpretations in ambiguous cases. For example, a static image may capture a person mid-motion with a frisbee, which can plausibly be interpreted as either "throwing" or "catching". When only "catching" is annotated, the other, though equally plausible for the image, is marked incorrect when exact matching is used. As a result, correct predictions might be penalized, affecting both VLMs and HOI-specific methods. To avoid penalizing valid predictions, we introduce a new benchmark that reformulates HOI detection as a multiple-answer multiple-choice task, where each question includes only ground-truth positive options and a curated set of negatives that are constructed to reduce ambiguity (e.g., when "catching" is annotated, "throwing" is not selected as a negative to avoid penalizing valid predictions). The proposed evaluation protocol is the first of its kind for both VLMs and HOI methods, enabling direct comparison and offering new insight into the current state of progress in HOI understanding.

EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection

Detecting Human-Object Interactions (HOI) in zero-shot settings, where models must handle unseen classes, poses significant challenges. Existing methods that rely on aligning visual encoders with large Vision-Language Models (VLMs) to tap into the extensive knowledge of VLMs, require large, computationally expensive models and encounter training difficulties. Adapting VLMs with prompt learning offers an alternative to direct alignment. However, fine-tuning on task-specific datasets often leads to overfitting to seen classes and suboptimal performance on unseen classes, due to the absence of unseen class labels. To address these challenges, we introduce a novel prompt learning-based framework for Efficient Zero-Shot HOI detection (EZ-HOI). First, we introduce Large Language Model (LLM) and VLM guidance for learnable prompts, integrating detailed HOI descriptions and visual semantics to adapt VLMs to HOI tasks. However, because training datasets contain seen-class labels alone, fine-tuning VLMs on such datasets tends to optimize learnable prompts for seen classes instead of unseen ones. Therefore, we design prompt learning for unseen classes using information from related seen classes, with LLMs utilized to highlight the differences between unseen and related seen classes. Quantitative evaluations on benchmark datasets demonstrate that our EZ-HOI achieves state-of-the-art performance across various zero-shot settings with only 10.35% to 33.95% of the trainable parameters compared to existing methods. Code is available at https://github.com/ChelsieLei/EZ-HOI.

Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI

Detecting human-object interactions (HOI) in a few-shot setting remains a challenge. Existing meta-learning methods struggle to extract representative features for classification due to the limited data, while existing few-shot HOI models rely on HOI text labels for classification. Moreover, some query images may display visual similarity to those outside their class, such as similar backgrounds between different HOI classes. This makes learning more challenging, especially with limited samples. Bongard-HOI (Jiang et al. 2022) epitomizes this HOI few-shot problem, making it the benchmark we focus on in this paper. In our proposed method, we introduce novel label-uncertain query augmentation techniques to enhance the diversity of the query inputs, aiming to distinguish the positive HOI class from the negative ones. As these augmented inputs may or may not have the same class label as the original inputs, their class label is unknown. Those belonging to a different class become hard samples due to their visual similarity to the original ones. Additionally, we introduce a novel pseudo-label generation technique that enables a mean teacher model to learn from the augmented label-uncertain inputs. We propose to augment the negative support set for the student model to enrich the semantic information, fostering diversity that challenges and enhances the student's learning. Experimental results demonstrate that our method sets a new state-of-the-art (SOTA) performance by achieving 68.74% accuracy on the Bongard-HOI benchmark, a significant improvement over the existing SOTA of 66.59%. In our evaluation on HICO-FS, a more general few-shot recognition dataset, our method achieves 73.27% accuracy, outperforming the previous SOTA of 71.20% in the 5-way 5-shot task.