Research map / Vision-language and multimodal / CLIP and zero-shot
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SimVLM huge instead of the strongest reported baseline | Vision-language pretraining · VQA test-std | vqa-score: 76.60 -> 80.34 | Helped |
| Use SimVLM huge instead of the strongest reported baseline | Vision-language pretraining · NLVR2 test-P | accuracy: 83.98 -> 85.15 | Helped |
| Use SimVLM huge instead of the strongest reported baseline | Vision-language pretraining · SNLI-VE test | accuracy | Helped |
| Use SimVLM huge instead of the strongest reported baseline | Vision-language pretraining · CoCo Caption Karpathy test | CIDEr: 140.9 -> 143.3 | Helped |
| Use SimVLM huge instead of the strongest reported baseline | Vision-language pretraining · NoCaps validation | CIDEr: 92.5 -> 110.3 | Helped |
| Use SimVLM huge instead of the strongest reported baseline | Vision-language pretraining · Multi30k En-De | BLEU@4 | Helped |
| Add pretraining (ablation) | SimVLM small · VQA | VQA score: 49.70 -> 67.43 | Helped |
| Use an encoder-decoder rather than decoder-only architecture (ablation) | SimVLM small · VQA | VQA score: 65.23 -> 67.43 | Helped |
| Add noisy image-text pretraining data (ablation) | SimVLM small · VQA | VQA score: 49.23 -> 67.43 | Helped |
| Add text-only pretraining data (ablation) | SimVLM small · VQA | VQA score: 65.25 -> 67.43 | Helped |
| Add the convolutional image-patch stage (ablation) | SimVLM small · VQA | VQA score: 63.11 -> 67.43 | Helped |
| Use PrefixLM rather than span corruption (ablation) | SimVLM small · VQA | VQA score: 66.23 -> 67.43 | Helped |
Closest papers by meaning
- CogVLM: Visual Expert for Pretrained Language Models (NeurIPS 2024 · similarity 0.92)
- Revisiting the Role of Language Priors in Vision-Language Models (ICML 2024 · similarity 0.92)
- Probing Visual Language Priors in VLMs (ICML 2025 · similarity 0.92)
- Vision‑Language‑Vision Auto‑Encoder: Scalable Knowledge Distillation from Diffusion Models (NeurIPS 2025 · similarity 0.91)
- Unveiling Encoder-Free Vision-Language Models (NeurIPS 2024 · similarity 0.91)
- VILA: On Pre-training for Visual Language Models (CVPR 2024 · similarity 0.91)
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