Omni-RGPT: Unifying Image and Video Region-level Understanding via Token Marks

We present Omni-RGPT, a multimodal large language model designed to facilitate region-level comprehension for both images and videos. To achieve consistent region representation across spatio-temporal dimensions, we introduce Token Mark, a set of discretized tokens highlighting the target regions within the visual feature space. These tokens are directly embedded into spatial regions using region prompts (e.g., boxes or masks) and simultaneously incorporated into the text prompt to specify the target, establishing a direct connection between visual and language tokens. To further support robust video understanding without requiring tracklets, we introduce an auxiliary task that guides Token Mark by leveraging the consistency of the tokens, enabling stable region interpretation across video sequences.Additionally, we introduce RegVID-300k, a large-scale region-level video instruction dataset curated from diverse public video sources. Omni-RGPT achieves state-of-the-art results on visual commonsense reasoning benchmarks in image-based (VCR) and video-based (Causal-VidQA) tasks while also demonstrating strong performance in captioning and Referring Expression Comprehension (REC) tasks.