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×10−51\times 10^{-5} for Stage-1 and Stage-2, respectively. In Stage-2, we fine-tune LoRA [25] with a rank of 1616 applied to the vision encoder. The batch size is fixed at 1616 for all experiments, and the total number of training steps is 46004600. Competitors. Our method enhances visual representation through diffusion-based reconstruction. We compare it with three state-of-the-art methods: DIVA [77], GenHancer [57], and un, CLIP [42], leading to the missing entries. LLM CLIP Vision-Centric Benchmarks Conventional MLLM Benchmarks MMVP- MLLM [75]
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Naive combinations of diffusion and contrastive learning suffer from gradient conflict, but injecting contrastive signals from reconstructed images can jointly optimize discriminative and perceptual abilities in CLIP visual encoders.
