arXiv:2609.36348v1 Announce Type: cross Abstract: Generative and representation learning remain asymmetrically connected: semantic representations are used to improve diffusion generation, whereas th...
The paper introduces CARE, a lightweight, plug‑and‑play regularization framework for diffusion models that dynamically adjusts feature distributions based on condition similarity. By leveraging built‑in conditioning signals such as labels or text prompts, CARE promotes tighter feature clusters for similar conditions without requiring explicit alignment losses or external supervision. Empirical results show consistent improvements in visual fidelity and convergence stability, achieving significant FID reductions and speed‑ups on ImageNet and text‑to‑image tasks, and it can be combined with existing regularization methods for further gains.
arXiv:2509. 23876v3 Announce Type: replace-cross Abstract: Autoregressive (AR) models based on next-scale prediction have emerged as a powerful tool for image generation, but they face a critical weakness: information inconsistencies between patches across timesteps introduced by progressive resolution scaling.
arXiv:2507. 02288v2 Announce Type: replace-cross Abstract: Domain Generalization (DG) seeks to develop a versatile model capable of performing effectively on unseen target domains.
arXiv:2610.09502v1 Announce Type: new Abstract: Open-vocabulary detection (OVD) recognizes categories unseen during training through textual category queries, yet achieving strong generalization with...
arXiv:2610.20608v1 Announce Type: new Abstract: Representation-alignment methods effectively accelerate the diffusion transformer training. REPA, for example, uses a pretrained vision encoder to supe...
Contrastive Language-Image Pre-training (CLIP) has been shown to have limitations in its fine-grained dense feature representation, due to its pre-training focusing on matching the whole image to a text description. Considering the large data and computational burden in pre-training a vision-language model from scratch, a series of works aim to enhance the fine-grained ability of CLIP through a fine-tuning scheme.
ProCAP introduces a probabilistic cross-attentive prompt learning framework for vision-language models like CLIP, enabling improved cross-modal interaction without updating the backbone. It jointly learns visual and textual prompt tokens, linking them via stacked bidirectional multi-head cross-attention to refine each branch across prompt depth. The method incorporates Gaussian parameterization of prompt tokens, lightweight KL and L2 regularization, and a compact symmetric InfoNCE head to align image features with class-level text representations, achieving strong few-shot base-to-novel performance and competitive transfer results across multiple datasets and benchmarks.
Open-vocabulary detection (OVD) recognizes categories unseen during training through textual category queries, yet achieving strong generalization with real-time efficiency remains challenging. Beyond...
AcFlow introduces an inference‑time controller for text‑to‑image diffusion transformers that transports intermediate layer activations through a learned, concept‑conditioned velocity field while keeping the base model frozen. The method allows fine‑grained style intensity control and suppression of unwanted concepts, achieving superior style–content trade‑offs compared to baselines and generalizing to unseen concepts without per‑concept fitting. Experiments demonstrate improved style alignment and qualitative suppression of diverse concepts where direct prompting fails.
arXiv:2607. 00371v1 Announce Type: cross Abstract: Visual AutoRegressive modeling (VAR) has pioneered a coarse-to-fine multi-scale autoregressive generative paradigm, demonstrating strong capabilities in image generation.
arXiv:2512.23245v3 Announce Type: replace Abstract: Recent text-to-image diffusion models have significantly improved visual quality and text alignment. However, generating a sequence of images while...