Vision-Language Model (VLM) is now an important component to enable robust robot manipulation. Yet, using it to translate human instructions into an action-resolvable intermediate representation often needs a tradeoff between VLM-comprehensibility and generalizability. Inspired by context-free grammar structure, we design the Semantic Assembly representation named SEAM, by decomposing the intermediate representation into vocabulary and grammar. Doing so leads us to a concise vocabulary of semantically-rich operations and a VLM-friendly grammar for handling diverse unseen tasks. Also, we design a novel open-vocabulary segmentation paradigm with an in-context learning strategy to precisely localize fine-grained object parts for manipulation (e.g., cup handle, teapot opening) effectively with the shortest inference time over all state-of-the-art parallel works. We then formulate new metrics for action-generalizability and VLM-comprehensibility to evaluate mainstream representations, demonstrating the strong performance of SEAM on both aspects. Extensive real-world experiments further manifest the SOTA performance of SEAM under varying settings and tasks.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Recent advancements in the text-rendering capabilities of image generation models have made the end-to-end creation of graphic design content, such as posters, increasingly feasible. However, existing reward models fall short of accurately assessing design quality, as they primarily focus on global image aesthetics while overlooking the critical dimensions of typography and layout. Furthermore, the scarcity of domain-specific preference data remains a significant bottleneck, limiting the further development of graphic design evaluation and generation. To bridge this gap, we design an automated pipeline to construct a high-quality dataset of 70k poster preferences by leveraging the consensus of multiple Multi-modal Large Language Models (MLLMs) to simulate human-like judgment. Based on this dataset, we propose PosterReward, a reward model specifically designed for high-precision poster assessment through a cascaded, multi-stage training strategy. We also provide multiple variants of the model to cater to different application scenarios. Finally, we introduce PosterRewardBench and PosterBench to evaluate the performance of existing reward models in poster assessment and the generation capabilities of current text-to-image models in poster creation, respectively.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表