Same or Not? Enhancing Visual Perception in Vision-Language Models

• URL: http://arxiv.org/abs/2512.23592v1
• Date: Mon, 29 Dec 2025 16:43:47 GMT
• Title: Same or Not? Enhancing Visual Perception in Vision-Language Models
• Authors: Damiano Marsili, Aditya Mehta, Ryan Y. Lin, Georgia Gkioxari,
• Abstract summary: Vision-language models (VLMs) excel at broad visual understanding but remain coarse-grained, exhibit visual biases, and miss subtle visual details.<n>To address this, we introduce a new training corpus and task designed to enhance the perceptual abilities ofVLMs.<n> TWIN is a large-scale dataset of 561,000 image-pair queries that task models to determine whether two visually similar images depict the same object.
• Score: 6.971464056247448
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) excel at broad visual understanding but remain coarse-grained, exhibit visual biases, and miss subtle visual details. Existing training corpora reinforce this limitation by emphasizing general recognition ("Is it a cat or a dog?") over fine-grained perception. To address this, we introduce a new training corpus and task designed to enhance the perceptual abilities of VLMs. TWIN is a large-scale dataset of 561,000 image-pair queries that task models to determine whether two visually similar images depict the same object, encouraging attention to nuanced visual cues. The dataset spans a diverse range of everyday objects across contexts, viewpoints, and appearances. Fine-tuning VLMs on TWIN yields notable gains in fine-grained recognition, even on unseen domains such as art, animals, plants, and landmarks. To quantify these gains, we introduce FGVQA, a benchmark suite of 12,000 queries that repurposes fine-grained recognition and retrieval datasets from multiple domains. While existing VLMs struggle on FGVQA, when fine-tuned on TWIN they improve by up to 19.3%, without compromising performance on general VQA benchmarks. Finally, our TWIN dataset scales favorably with object annotations, and our analysis shows that scale is key to performance. We envision TWIN as a drop-in addition to open-source VLM training corpora, advancing perceptual precision of future models. Project webpage: https://glab-caltech.github.io/twin/

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