12 accepted papers
Vision-Language Models (VLMs), exemplified by CLIP, have emerged as foundational for multimodal intelligence. However, their capacity for logical understanding remains significantly underexplored, resulting in critical **
Unlocking the spatial intelligence of multimodal large language models (MLLMs) is crucial for understanding and interacting with the 3D world. Prevailing approaches typically inject spatial priors via external tools, which impose significant inference overhead, or rely on latent feature distillation
Understanding and decoding human brain activity from electroencephalography (EEG) signals is a fundamental problem in neuroscience and artificial intelligence, with applications ranging from cognition and emotion recognition to clinical diagnosis and brain–computer interfaces. While recent EEG found…
Modeling task-driven attention in driving is a fundamental challenge for both autonomous vehicles and cognitive science. Existing methods primarily predict where drivers look by generating spatial heatmaps, but fail to capture the cognitive motivations behind attention allocation in specific context…
Modeling driver attention allocation by scanpath prediction plays a crucial role in advancing autonomous driving capabilities and enhancing accident anticipation. Existing studies primarily predict human scanpath for visual search, visual question answering, and free-viewing. Few studies have focuse…
Existing investigations on action understanding have made noteworthy advancements by treating activities as holistic events occurring in videos. However, these investigations have limited ability to comprehensively extract and represent human experiential knowledge, which hampers various practical a…
Scanpath refers to the trajectory of eye fixations when humans perform visual reasoning. Most existing methods mainly focus on predicting static attention maps, which represent the probability that each pixel in the image is paid attention to by humans. However, human gaze behavior is purposeful and…
Most existing attention prediction research focuses on salient instances like humans and objects. However the more complex interaction-oriented attention arising from the comprehension of interactions between instances by human observers remains largely unexplored. This is equally crucial for advanc…
Even with a still image, humans can ratiocinate various visual cause-and-effect descriptions before, at present, and after, as well as beyond the given image. However, it is challenging for models to achieve such task–the visual event ratiocination, owing to the limitations of time and space. To thi…
Credit card transaction fraud costs billions of dollars to card issuers every year. Besides, the credit card transaction dataset is very skewed, there are much fewer samples of frauds than legitimate transactions. Due to the data security and privacy, different banks are usually not allowed to share…
Visual reasoning between visual image and natural language description is a long-standing challenge in computer vision. While recent approaches offer a great promise by compositionality or relational computing, most of them are oppressed by the challenge of training with datasets containing only a l…
Facial landmark detection, head pose estimation, and facial deformation analysis are typical facial behavior analysis tasks in computer vision. The existing methods usually perform each task independently and sequentially, ignoring their interactions. To tackle this problem, we propose a unified fra…