Multimodal sarcasm detection is a complex task that requires distinguishing subtle complementary signals across modalities while filtering out irrelevant information. Many advanced methods rely on learning shortcuts from datasets rather than extracting intended sarcasm-related features. However, our experiments show that shortcut learning impairs the model's generalization in real-world scenarios. Furthermore, we reveal the weaknesses of current modality fusion strategies for multimodal sarcasm detection through systematic experiments, highlighting the necessity of focusing on effective modality fusion for complex emotion recognition. To address these challenges, we construct MUStARD++R by removing shortcut signals from MUStARD++. Then, a Multimodal Conditional Information Bottleneck (MCIB) model is introduced to enable efficient multimodal fusion for sarcasm detection. Experimental results show that the MCIB achieves the best performance without relying on shortcut learning.

Traditional dense video captioning predominantly focuses on edited exocentric footage. These videos are filmed from an external perspective and generally feature distinct transitions between different events, as exemplified in edited instructional videos. However, such videos do not genuinely reflect the way we perceive our real lives. Instead, we observe the world from an egocentric viewpoint and witness only continuous unedited footage. To facilitate further research, we introduce a new topic: Egocentric Vehicle Dense Video Captioning, in classic vehicle driving scenarios. This is a multi-modal, multi-task subject endeavor for a comprehensive understanding of untrimmed, egocentric driving videos. It consists of three sub-tasks that concentrate on event localization, captioning, and vehicle state estimation separately. To accomplish these tasks, it is necessary to deal with at least three challenges: extracting ego-motion relevant information, describing driving behavior and analyzing the underlying rationale, as well as resolving the boundary ambiguity problem. In response, we devise corresponding solutions, including a vehicle ego-motion learning strategy and a novel adjacent contrastive learning strategy, which effectively address the aforementioned issues. We validate our method by conducting extensive experiments on the BDD-X dataset, all of which show promising results and achieve new state-of-the-art performance on most metrics, which proves the effect of our approach.