4 papers
Position: Reasoning After Perception Means Reasoning Without Vision
Hongcheng Gao, Zihao Huang, Jingyi Tang +12
A common belief in multimodal research is that the perceptual weaknesses of vision--language models can be compensated by stronger language reasoning (e.g., chain-of-thought, in-co…
SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks
Hongcheng Gao, Hailong Qu, Jingyi Tang +18
Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world. However, existing benchmarks predomin…
Aligning What EEG Can See: Structural Representations for Brain-Vision Matching
Jingyi Tang, Shuai Jiang, Fei Su +1
Visual decoding from electroencephalography (EEG) has emerged as a highly promising avenue for non-invasive brain-computer interfaces (BCIs). Existing EEG-based decoding methods pr…
Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigation
Hongcheng Gao, Jiashu Qu, Jingyi Tang +6
The hallucination of large multimodal models (LMMs), providing responses that appear correct but are actually incorrect, limits their reliability and applicability. This paper aims…