Data-free model stealing involves replicating the functionality of a target model into a substitute model without accessing the target model's structure, parameters, or training data. Instead, the adversary can only access the target model's predictions for generated samples. Once the substitute model closely approximates the behavior of the target model, attackers can exploit its white-box characteristics for subsequent malicious activities, such as adversarial attacks. Existing methods within cooperative game frameworks often produce samples with high confidence for the prediction of the substitute model, which makes it difficult for the substitute model to replicate the behavior of the target model. This paper presents a new data-free model stealing approach called Query Efficient Data Generation (QEDG). We introduce two distinct loss functions to ensure the generation of sufficient samples that closely and uniformly align with the target model's decision boundary across multiple classes. Building on the limitation of current methods, which typically yield only one piece of supervised information per query, we propose the query-free sample augmentation that enables the acquisition of additional supervised information without increasing the number of queries. Motivated by theoretical analysis, we adopt the consistency rate metric, which more accurately evaluates the similarity between the substitute and target models. We conducted extensive experiments to verify the effectiveness of our proposed method, which achieved better performance with fewer queries compared to the state-of-the-art methods on the real MLaaS scenario and five datasets.

Recently, with the rapid advancement of multimodal large language models (MLLMs), intent-oriented video captioning has received increasing attention due to its potential for controllable and grounded visual understanding. Fine-grained localized video captioning presents unique challenges due to the need for controllability, object grounding, and temporal precision. In this paper, we propose MGVC, a two-stage framework for intention-oriented controllable video captioning in the IntentVC 2025 Challenge. Our pipeline first leverages a fine-tuned MLLM to generate diverse preliminary captions. These candidate captions are then refined by another finetuned MLLM for further semantic alignment and stylistic coherence. We introduce a video-text matching module, further finetuned on the IntentVC dataset. This module will filter out semantically misaligned candidate captions. For caption selection, we train category-specific regressors that predict caption quality scores based on VTM similarity, textual features, intra-caption BLEU, and CLIP-based retrieval correlations. The caption with the highest predicted alignment score is chosen as final output. Finally, our method achieves 1st place in the IntentVC 2025 Grand Challenge, which demonstrates the effectiveness and generalization of our proposed method.

Audio-visual question answering aims to answer questions regarding both audio and visual modalities in a given video, and has drawn increasing research interest in recent years. However, there have been no appropriate datasets for this challenging task on videos in real-life scenarios so far. They are either designed with questions containing only visual clues without taking any audio information into account, or considering audio with restrictions to specific scenarios, such as panoramic videos and videos about music performances. In this paper, to overcome the limitations of existing datasets, we introduce AVQA, a new audio-visual question answering dataset on videos in real-life scenarios. We collect 57,015 videos from daily audio-visual activities and 57,335 specially-designed question-answer pairs relying on clues from both modalities, where information contained in a single modality is insufficient or ambiguous. Furthermore, we propose a Hierarchical Audio-Visual Fusing module to model multiple semantic correlations among audio, visual, and text modalities and conduct ablation studies to analyze the role of different modalities on our datasets. Experimental results show that our proposed method significantly improves the audio-visual question answering performance over various question types. Therefore, AVQA can provide an adequate testbed for the generation of models with a deeper understanding of multimodal information on audio-visual question answering in real-life scenarios. (The dataset is available at https://mn.cs.tsinghua.edu.cn/avqa)