Efficient Visual Pointing for Embodied AI:Agent-Driven Data Synthesis, Cross-Block Attention, and Iterative Correction
2026-06-29 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a method called PointArena 2026 to help AI understand language instructions by pointing to exact pixels in images. They improved their system by creating large sets of example data, verifying these examples carefully, and using special model parts to fix common mistakes. Their approach combines different expert models to handle tasks like recognizing objects, understanding spatial relations, reasoning, counting, and controlling direction. This method achieved a high accuracy and ranked second on a benchmark for visual pointing tasks.
visual pointingembodied AIsemantic candidate poolsdeterministic data pipelineattention mechanismcoordinate groundingcategory-aware routingspatial reasoningaffordancemodel validation
Authors
Zijian Hong, Qi Lv, Yuxiang Xie, Jianming Xing, Xiang Deng, Weili Guan, Liqiang Nie
Abstract
Visual pointing maps a language instruction to pixel co ordinates, a core skill for embodied AI. We describe our PointArena 2026 solution, which achieves 77.2% overall accuracy and ranks second on the benchmark. The ap proach targets three failure modes. First, agent-driven syn thesis builds large semantic and anchor-relative candidate pools; the server inventory contains 55,372 processed out puts, 53,772 de-duplicated sample IDs, and 37,574 train able completed or accepted rows. Second, a determinis tic steerable-data pipeline creates a verified 10,000-sample main set, plus reserve samples, using masks, templates, and path verification. Third, two model-side modules address complementary errors: AttnRes adds gated cross-block at tention for steerability, while ABC correction encodes per turbed coordinates with visual features for general coordi nate grounding. Category-aware routing combines comple mentary specialists; local validation used to select experts records 93.9% Affordance, 82.6% Spatial Relation, 78.2% Reasoning, 70.4% Counting, and 63.0% Steerability.