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GSR-BENCH: A Benchmark for Grounded Spatial Reasoning Evaluation via Multimodal LLMs
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The ability to understand and reason about spatial relationships between objects in images is an important component of visual reasoning. This skill rests on the ability to recognize and localize objects of interest and determine their spatial relation. Early vision and language models (VLMs) have been shown to struggle to recognize spatial relations. We extend the previously released What'sUp dataset and propose a novel comprehensive evaluation for spatial relationship understanding that highlights the strengths and weaknesses of 27 different models. In addition to the VLMs evaluated in What'sUp, our extensive evaluation encompasses 3 classes of Multimodal LLMs (MLLMs) that vary in their parameter sizes (ranging from 7B to 110B), training/instruction-tuning methods, and visual resolution to benchmark their performances and scrutinize the scaling laws in this task.
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Cited by 5 Pith papers
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Visual Credit Audit for Multimodal Spatial Reasoning
VCA finds 12.73–26.25% of spatial decisions are correct yet uncredited by the image, and separates marginal image support from relation-specific visual response.
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SpatialLLM, trained with 3D-aware orientation and distance data across multiple stages, scores 62.7% on the new SpatialVQA benchmark, surpassing GPT-4o by 8.7%.
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CAPTURe: Evaluating Spatial Reasoning in Vision Language Models via Occluded Object Counting
CAPTURe, a new benchmark for occluded pattern counting, shows that six vision-language models count far worse when objects are hidden, while humans make almost no errors.
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SpatialCoT: Advancing Spatial Reasoning through Coordinate Alignment and Chain-of-Thought for Embodied Task Planning
A two-stage VLM fine-tuning approach, coordinate alignment plus chain-of-thought grounding, improves closed-loop navigation and manipulation success rates over prior point-based spatial reasoning methods.
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OrientSAM: Mitigating Camera-Centric Shortcut in Multimodal Spatial Reasoning via Orientation-Aware Spatial Alignment
OrientSAM injects Fourier-encoded object orientation into a vision-language model and uses curriculum training, improving reference-centric spatial reasoning and reducing camera-centric shortcut behavior.
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