9 accepted papers
Composed Image Retrieval (CIR) aims to retrieve target images from candidate set using a hybrid-modality query consisting of a reference image and a relative caption that describes the user intent. Recent studies attempt to utilize Vision-Language Pre-training Models (VLPMs) with various fusion stra…
In communication systems, the challenge of ensuring accurate data transmission across noisy channels remains paramount. While semantic communication shows potential in improving image transmission and reconstruction, existing methods still suffer from perceptual quality degradation in high-noise env…
Contrastive Language-Image Pre-training (CLIP) achieves impressive performance on tasks like image classification and image-text retrieval by learning on large-scale image-text datasets. However, CLIP struggles with dense prediction tasks due to the poor grasp of the fine-grained details. Although e…
Image retrieval with composed query (IR-CQ) is a challenging task since it aims to retrieve the target image according to a hybrid-modality query which consists of a reference image and a text modifier. Previous approaches mainly focus on designing various multi-modal fusion modules to fuse the hybr…
Although denoising diffusion probabilistic models (DDPMs) have shown remarkable progress in image generation, they typically face two main challenges: the time-expensive sampling process and the semantically meaningless latent space, which are often addressed separately in previous works. In particu…
Large-scale vision-language pre-trained models have shown promising transferability to various downstream tasks. As the size of these foundation models and the number of downstream tasks grow, the standard full fine-tuning paradigm becomes unsustainable due to heavy computational and storage costs.…
This work introduces Video Diffusion Transformer (VDT), which pioneers the use of transformers in diffusion-based video generation. It features transformer blocks with modularized temporal and spatial attention modules to leverage the rich spatial-temporal representation inherited in transformers. A…
Deploying large-scale pre-trained models in the prompt-tuning paradigm has demonstrated promising performance in few-shot learning. Particularly, vision-language pre-training models (VL-PTMs) have been intensively explored in various few-shot downstream tasks. However, most existing works only apply…
A deep facial attribute editing model strives to meet two requirements: (1) attribute correctness -- the target attribute should correctly appear on the edited face image; (2) irrelevance preservation -- any irrelevant information (e.g., identity) should not be changed after editing. Meeting both re…