Adriel Kuek

Rui Cao, Ming Shan Hee, Adriel Kuek et al.

Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. However, with increasing model sizes, it becomes important to leverage powerful PVLMs more efficiently, rather than simply fine-tuning them. Recently, researchers have attempted to convert meme images into textual captions and prompt language models for predictions. This approach has shown good performance but suffers from non-informative image captions. Considering the two factors mentioned above, we propose a probing-based captioning approach to leverage PVLMs in a zero-shot visual question answering (VQA) manner. Specifically, we prompt a frozen PVLM by asking hateful content-related questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection. The good performance of models with Pro-Cap on three benchmarks validates the effectiveness and generalization of the proposed method.

Yuriel Ryan, Hei Man Ip, Adriel Kuek et al.

Current vision language models face hallucination and robustness issues against ambiguous or corrupted modalities. We hypothesize that these issues can be addressed by exploiting the shared information between modalities to compensate for the impaired one. To this end, we analyze multimodal interactions -- redundant (shared), unique (exclusive), and synergistic (emergent) task-relevant information provided by the modalities -- to determine their impacts on model reliability. Specifically, amplifying redundant interactions would increase this exploitable shared information to resolve these issues; yet, modern instruction datasets often eliminate redundancies to prioritize visual grounding. We bridge this gap through a self-captioning workflow featuring a \textsc{Multimodal Interaction Gate}: a mechanism to convert unique interactions into redundant interactions. Our findings suggest that increasing redundancy can reduce visual induced errors by 38.3\% and improve consistency by 16.8\%.

Xuanming Cui, Jaiminkumar Ashokbhai Bhoi, Chionh Wei Peng et al.

Dynamic Scene Graph Generation (DSGG) for videos is a challenging task in computer vision. While existing approaches often focus on sophisticated architectural design and solely use recall during evaluation, we take a closer look at their predicted scene graphs and discover three critical issues with existing DSGG methods: severe precision-recall trade-off, lack of awareness on triplet importance, and inappropriate evaluation protocols. On the other hand, recent advances of Large Multimodal Models (LMMs) have shown great capabilities in video understanding, yet they have not been tested on fine-grained, frame-wise understanding tasks like DSGG. In this work, we conduct the first systematic analysis of Video LMMs for performing DSGG. Without relying on sophisticated architectural design, we show that LMMs with simple decoder-only structure can be turned into State-of-the-Art scene graph generators that effectively overcome the aforementioned issues, while requiring little finetuning (5-10% training data).