Object state understanding aims at recognizing the co-occurrence and transitions of multiple object states in videos. While learning from videos handles seen object states well, it struggles with novel ones. We address this task in a zero-shot setting by extracting state-specific knowledge from pre-trained models and using Vision-Language Models (VLMs) to verify whether such knowledge is visually grounded in videos. However, the extracted knowledge varies in its ability to distinguish states, and VLM observations are not always trustworthy. To address this issue, we propose a trust-aware knowledge-guided method to model knowledge trustworthiness and emphasize highly discriminative knowledge that VLMs can reliably observe. Specifically, we collect spatial knowledge for each object state from retrieved images and cues generated from a Large Language Model, then use VLMs to vote on each knowledge element by scoring its visual consistency with the video. In addition to a single scene, temporal dependencies of object states across scenes are also captured using a generative VLM. Under spatial and temporal constraints, we propose an adaptive knowledge refinement module that iteratively updates knowledge reliability weights to achieve a global consensus in object state inference across the video. Finally, object states are inferred by combining the refined weights with VLM voting results. Experiments on two datasets demonstrate the effectiveness of our method.

The exploration of various vision-language tasks, such as visual captioning, visual question answering, and visual commonsense reasoning, is an important area in artificial intelligence and continuously attracts the attention of the research community. Despite improvements in overall performance, classic challenges still exist in vision-language tasks and hinder the development of this area. In recent years, the rise of pre-trained models is driving the research on vision-language tasks. Because of the massive scale of training data and model parameters, pre-trained models have exhibited excellent performance in numerous downstream tasks. Inspired by the powerful capabilities of pre-trained models, new paradigms have emerged to solve the classic challenges. Such methods have become mainstream in current research with increasing attention and rapid advances. In this paper, we present a comprehensive overview of how vision-language tasks benefit from pre-trained models. First, we review several main challenges in vision-language tasks and discuss the limitations of previous solutions before the era of pre-training. Next, we summarize the recent advances in incorporating pre-trained models to address the challenges in vision-language tasks. Finally, we analyze the potential risks associated with the inherent limitations of pre-trained models, discuss possible solutions, and attempt to provide future research directions.

Unsupervised image captioning aims to generate descriptions of images without relying on any image-sentence pairs for training. Most existing works use detected visual objects or concepts as bridge to connect images and texts. Considering that the relationship between objects carries more information, we use the object relationship as a more accurate connection between images and texts. In this paper, we adapt the idea of distant supervision that extracts the knowledge about object relationships from an external corpus and imparts them to images to facilitate inferring visual object relationships, without introducing any extra pre-trained relationship detectors. Based on these learned informative relationships, we construct pseudo image-sentence pairs for captioning model training. Specifically, our method consists of three modules: (1) a relationship learning module that learns to infer relationships from images under the distant supervision; (2) a relationship-to-sentence module that transforms the inferred relationships into sentences to generate pseudo image-sentence pairs; (3) an image captioning module that is trained by using the generated image-sentence pairs. Promising results on three datasets show that our method outperforms the state-of-the-art methods of unsupervised image captioning.

Exploiting relationships between objects for image and video captioning has received increasing attention. Most existing methods depend heavily on pre-trained detectors of objects and their relationships, and thus may not work well when facing detection challenges such as heavy occlusion, tiny-size objects, and long-tail classes. In this paper, we propose a joint commonsense and relation reasoning method that exploits prior knowledge for image and video captioning without relying on any detectors. The prior knowledge provides semantic correlations and constraints between objects, serving as guidance to build semantic graphs that summarize object relationships, some of which cannot be directly perceived from images or videos. Particularly, our method is implemented by an iterative learning algorithm that alternates between 1) commonsense reasoning for embedding visual regions into the semantic space to build a semantic graph and 2) relation reasoning for encoding semantic graphs to generate sentences. Experiments on several benchmark datasets validate the effectiveness of our prior knowledge-based approach.