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Found 14 papers, 5 papers with code
LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation
To overcome the difficult multimodal fusion of image and layout, we propose to construct a structural image patch with region information and transform the patched image into a special layout to fuse with the normal layout in a unified form.
Ranked #1 on
Layout-to-Image Generation
on Visual Genome 128x128
Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection
Thus, the optimum of the distillation loss does not necessarily lead to the optimal student classification scores for dense object detectors.
SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation
Experimental results on Stanford2D3D Panoramic datasets show that SGAT4PASS significantly improves performance and robustness, with approximately a 2% increase in mIoU, and when small 3D disturbances occur in the data, the stability of our performance is improved by an order of magnitude.
Ranked #4 on
Semantic Segmentation
on Stanford2D3D Panoramic
Epoch-evolving Gaussian Process Guided Learning
In this paper, we propose a novel learning scheme called epoch-evolving Gaussian Process Guided Learning (GPGL), which aims at characterizing the correlation information between the batch-level distribution and the global data distribution.
Windformer:Bi-Directional Long-Distance Spatio-Temporal Network For Wind Speed Prediction
History and future time information includes the trend of airflow changes, whether this dynamic information can be utilized will also affect the prediction effect.
Scene Learning: Deep Convolutional Networks For Wind Power Prediction by Embedding Turbines into Grid Space
There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction.
ResKD: Residual-Guided Knowledge Distillation
In this paper, we see knowledge distillation in a fresh light, using the knowledge gap, or the residual, between a teacher and a student as guidance to train a much more lightweight student, called a res-student.
Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
Compared with static views, abundant dynamic properties between video frames are beneficial to refined depth estimation, especially for dynamic objects.
Residual-guided Personalized Speech Synthesis based on Face Image
Experimental results show that the speech synthesized by our model is comparable to the personalized speech synthesized by training a large amount of audio data in previous works.
Self-paced Multi-grained Cross-modal Interaction Modeling for Referring Expression Comprehension
As an important and challenging problem in vision-language tasks, referring expression comprehension (REC) generally requires a large amount of multi-grained information of visual and linguistic modalities to realize accurate reasoning.
IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels
Secondly, the subdomain classifier module based on instance confidence is constructed, which can dynamically divide the target domain into easy and difficult subdomains according to the relative proportion of easy and difficult instances.
Emotional Reaction Intensity Estimation Based on Multimodal Data
In this paper, 1. better features are extracted with the SOTA pretrained models.
Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration
Segmentation of nodules in thyroid ultrasound imaging plays a crucial role in the detection and treatment of thyroid cancer.
SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model
Controllable spherical panoramic image generation holds substantial applicative potential across a variety of domains. However, it remains a challenging task due to the inherent spherical distortion and geometry characteristics, resulting in low-quality content generation. In this paper, we introduce a novel framework of SphereDiffusion to address these unique challenges, for better generating high-quality and precisely controllable spherical panoramic images. For the spherical distortion characteristic, we embed the semantics of the distorted object with text encoding, then explicitly construct the relationship with text-object correspondence to better use the pre-trained knowledge of the planar images. Meanwhile, we employ a deformable technique to mitigate the semantic deviation in latent space caused by spherical distortion. For the spherical geometry characteristic, in virtue of spherical rotation invariance, we improve the data diversity and optimization objectives in the training process, enabling the model to better learn the spherical geometry characteristic. Furthermore, we enhance the denoising process of the diffusion model, enabling it to effectively use the learned geometric characteristic to ensure the boundary continuity of the generated images. With these specific techniques, experiments on Structured3D dataset show that SphereDiffusion significantly improves the quality of controllable spherical image generation and relatively reduces around 35% FID on average.
