6 accepted papers
Understanding long-form videos, such as movies and TV episodes ranging from tens of minutes to two hours, remains a significant challenge for multi-modal models. Existing benchmarks often fail to test the full range of cognitive skills needed to process these temporally rich and narratively complex
In this paper, we introduce Part-Aware Point Grounded Description (PaPGD), a challenging task aimed at advancing 3D multimodal learning for fine-grained, part-aware segmentation grounding and detailed explanation of 3D objects. Existing 3D datasets largely focus on either vision-only part segmentati…
Diffusion models break down the challenging task of generating data from high-dimensional distributions into a series of easier denoising steps. Inspired by this paradigm, we propose a novel approach that extends the diffusion framework into modality space, decomposing the complex task of RGB image…
3D visual grounding is the ability to localize objects in 3D scenes conditioned by utterances. Most existing methods devote the referring head to localize the referred object directly, causing failure in complex scenarios. In addition, it does not illustrate how and why the network reaches the final…
Designing robust text-to-image (T2I) models have been extensively explored in recent years, especially with the emergence of diffusion models, which achieves state-of-the-art results on T2I synthesis tasks. Despite the significant effort and success in this direction, we observed that the existing m…
3D visual grounding task has been explored with visual and language streams to comprehend referential language for identifying targeted objects in 3D scenes. However, most existing methods devote the visual stream to capture the 3D visual clues using off-the-shelf point clouds encoders. The main que…