Subject-driven personalized text-to-image generation requires a pretrained diffusion model to acquire a specific subject from a few reference images while preserving subject identity, following novel text prompts, and maintaining sample diversity. Existing optimization-based methods instantiate subject adaptation through full fine-tuning, textual embedding optimization, or low-rank parameter updates; PaRa further constrains personalization from the perspective of parameter rank reduction.
arXiv:2609.40361v1 Announce Type: cross
Abstract: Multimodal large language models (MLLMs) are rapidly advancing clinical diagnosis, yet their adaptation pipelines remain anchored to accuracy-based o...
By Tian Xia, Minghao Liu, Yiqing Liang, Laixi Shi, Jiayun Wang
arXiv:2606. 16682v3 Announce Type: replace Abstract: When AI agents use language models to evaluate their own outputs in a feedback loop, systematic biases emerge.
By Zewen Liu
TextVBench is a new benchmark and evaluation framework for assessing how well video generation models render and preserve text. It introduces two tracks—TextVBench‑T2V for prompt‑specified text creation and maintenance, and TextVBench‑Ref2V for preserving text from reference images—using a multi‑target, multi‑instance spatiotemporal correspondence approach. The framework employs target‑conditioned candidate selection, supports multiple OCR regions per target, and separates spatial and temporal fidelity, summarizing results with a length‑weighted harmonic score and detailed diagnostics.
By Ruiqiang Zhang, Hengyi Wang, Chang Liu, Guanjie Wang, Zehua Ma, Weiming Zhang
The paper introduces PixelJev, a native-image decision interface that combines an image, a task instruction, and a runtime candidate set into a structured choice and candidate-conditioned probabilities using small open multimodal models. It unifies recognition and multiple-choice visual question answering via a language-model readout, offering options for frozen inference, language-side adaptation, and held-out calibration. Across seven benchmarks, 64-shot source adaptation significantly boosts Pets accuracy from 60.13% to 92.40%, and the system supports both VQA tasks with frozen inference, while also highlighting areas for improvement such as schema robustness and cross-family transfer.
By Xunlan Zhou, Xianliang Yang, Li Zhao
The paper introduces SatisDive, a training‑free inference method that balances reward and diversity in text‑to‑image diffusion by enforcing a reward floor for each image and a diversity cutoff for the batch. By adjusting the reward floor, the method traces a Pareto frontier between worst‑candidate reward and batch diversity. Experiments on Pick‑a‑Pic show that SatisDive consistently outperforms FK steering, improving worst‑candidate reward by up to 0.70 in some settings and Pareto‑dominating FK steering across overlapping DreamSim ranges.
By Kevin Zhai, Siva Rajesh Kasa, Soumya Roy, Sumit Negi, Mubarak Shah