Unverified50% confidenceOpinionExact time
Multimodal LLMs perform limitedly on pixel-level localization tasks (such as bounding box regression in object tracking) because their training objectives emphasize semantic understanding rather than spatial precision.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/29/2026
First Seen
Valid until: 1/27/2027
Sources
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Related Claims
UnverifiedThe core strengths of LLMs lie in language understanding, generation, and cross-task generalization, and their training objectives do not naturally match pixel-level visual localization.77% similarUnverifiedOpen recognition relying on general multimodal models may achieve lower accuracy than purpose-trained custom models in scenarios involving complex lighting, target occlusion, dense small objects, or industry-specific proprietary categories.74% similarUnverifiedThe training objectives of mainstream LMMs almost all revolve around a single visual input, taking in one video or one image and outputting the corresponding text, so their cross-sample reasoning ability is not explicitly optimized.72% similarUnverified1-bit/1.58-bit要求从训练阶段就以低精度权重为目标，与训练后量化方案本质不同72% similarUnverifiedLightweight detectors may need to be trained for specific models or specific tasks, and their cross-model transferability may be limited.72% similar
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