The Contrastive Language-Image Pretraining (CLIP) model has been widely used in various downstream vision tasks. The few-shot learning paradigm has been widely adopted to augment its capacity for these tasks. However, current paradigms may struggle with fine-grained classification, such as satellite image recognition, due to widening domain gaps. To address this limitation, we propose retrieval-enhanced visual prompt learning (RePrompt), which introduces retrieval mechanisms to cache and reuse the knowledge of downstream tasks. RePrompt constructs a retrieval database from either training examples or external data if available, and uses a retrieval mechanism to enhance multiple stages of a simple prompt learning baseline, thus narrowing the domain gap. During inference, our enhanced model can reference similar samples brought by retrieval to make more accurate predictions. A detailed analysis reveals that retrieval helps to improve the distribution of late features, thus, improving generalization for downstream tasks. RePrompt attains state-of-the-art performance on a wide range of vision datasets, including 11 image datasets, 3 video datasets, 1 multi-view dataset, and 4 domain generalization benchmarks.

The rapid advancement of generative AI has raised concerns about the authenticity of digital images, as highly realistic fake images can now be generated at low cost, potentially increasing societal risks. In response, several datasets have been established to train detection models aimed at distinguishing AI-generated images from real ones. However, existing datasets suffer from limited generalization, low image quality, overly simple prompts, and insufficient image diversity. To address these limitations, we propose a high-quality, large-scale dataset comprising over 730,000 images across multiple categories, including both real and AI-generated images. The generated images are synthesized via state-of-the-art methods, including text-to-image generation (guided by over 10,000 carefully designed prompts), image inpainting, image refinement, and face swapping. Each generated image is annotated with its generation method and category. Inpainting images further include binary masks to indicate inpainted regions, providing rich metadata for analysis. Compared to existing datasets, detection models trained on our dataset demonstrate superior generalization capabilities. Our dataset not only serves as a strong benchmark for evaluating detection methods but also contributes to advancing the robustness of AI-generated image detection techniques. Building upon this, we propose a lightweight detection method based on image noise entropy, which transforms the original image into an entropy tensor of Non-Local Means (NLM) noise before classification. Extensive experiments demonstrate that models trained on our dataset achieve strong generalization, and our method delivers competitive performance, establishing a solid baseline for future research. The dataset and source code are publicly available at https://real-hd.github.io.

3D scene reconstruction from 2D images has been a long-standing task. Instead of estimating per-frame depth maps and fusing them in 3D, recent researches leverage the neural implicit surface as a global representation for 3D reconstruction. Equipped with data-driven pre-trained geometric cues, these methods have demonstrated promising performance. However, the inevitable inaccurate estimation of priors can lead to suboptimal reconstruction quality, particularly in some geometrically complex regions. In this paper, we propose a two-stage training process to further improve the reconstruction quality. It decouples the view-dependent and view-independent colors, and leverages two novel consistency constraints to enhance detail reconstruction performance without requiring extra priors. Additionally, we introduce an essential mask scheme to adaptively influence the selection of supervision constraints, thereby improving performance in a self-supervised paradigm. Experiments on synthetic and real-world datasets show the capability of reducing the side effects of inaccurately estimated priors and achieving high-quality scene reconstruction with rich geometric details.

In the past years, significant improvements in the field of neural architecture search(NAS) have been made. However, in order to improve the performance of the model, many search spaces contain multi-branch architectures, which leads to the gap between the searched constraint and real inference time. In this work, we propose a re-parameterization (Rep) search space based on structural Rep techniques. In the Rep search space, the subnets are multi-branch in training and single-path in inference. Furthermore, RepNAS, a one-stage NAS approach, is present to efficiently search the optimal diverse branch block (ODBB) for each layer under the branch number constraint. Our experimental results show the searched ODBB can easily surpass the manual diverse branch block (DBB) with efficient training.

Networking pruning is widely utilized for accelerating the inference procedure of deep models in low-resource settings. In this paper, a novel Gradient-based method, Soft Taylor Pruning(STP), are proposed to reduce the network complexity in dynamic way. Given a global compression rate, all filters are categorized into two parts by the gradient-based evaluation criterion in the pruning process. Then, two types of filters are remixed and updated in the training epoch. When a pruning error occurs, the model can correct the pruning error by re-active pruned filters in the next training epoch. In this way, the capacity of the model channel space remains the same until the network structure converges. In order to reduce the impact of large-weighted filters on criterion, We take the absolute value of the product of the feature map and the gradient as the evaluation criterion. So as to reduce the model pruning time, STP allows simultaneous pruning on multiple layers by controlling the opening and closing of multiple mask layers. Moreover, STP can be applied to various advanced CNNs, such as MobileNet. The features of our method are: 1) maintain the integrity of the model channel space; 2) less time cost of model compression; 3)less dependence on the pretrained model.