Text-Guided Semantic Image Encoder

• URL: http://arxiv.org/abs/2511.20770v1
• Date: Tue, 25 Nov 2025 19:04:04 GMT
• Title: Text-Guided Semantic Image Encoder
• Authors: Raghuveer Thirukovalluru, Xiaochuang Han, Bhuwan Dhingra, Emily Dinan, Maha Elbayad,
• Abstract summary: We propose the Text-Guided Semantic Image (TIE), which generates image representations conditioned on the input text query.<n>TIE-based vision-language models (VLMs) attain superior performance while utilizing only half as many image tiles (tokens)<n>TIE consistently attends to query-relevant regions, enhancing both interpretability and query-specific grounding.
• Score: 25.15773515839525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image encoders, a fundamental component of vision-language models (VLMs), are typically pretrained independently before being aligned with a language model. This standard paradigm results in encoders that process images agnostically, without regard to the specific downstream task or text query. To address this limitation, we propose the Text-Guided Semantic Image Encoder (TIE), which generates image representations conditioned on the input text query. VLMs equipped with TIE outperform their conventional counterparts by +1.5 and +1.3 points on average across nine image-to-text benchmarks at the 1B and 3B scales, respectively, with gains reaching up to 6 points on tasks such as DocVQA and InfoVQA. Moreover, TIE-based VLMs attain superior performance while utilizing only half as many image tiles (tokens), resulting in notably improved inference efficiency. TIE also generalizes well with generic queries, indicating that text-conditioned training effectively optimizes the encoder to capture key visual features. Qualitative analysis confirms that TIE consistently attends to query-relevant regions, enhancing both interpretability and query-specific grounding.

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