6 accepted papers
Image denoising is a fundamental task in computer vision aimed at recovering clean images from noise-corrupted observations. While supervised deep learning methods achieve remarkable performance when trained on paired data with known noise levels, their real-world applicability is limited as noise c
The significance of visual emotion distribution learning (VEDL) has surged, particularly with the growing inclination to convey emotions through images. The key of VEDL lies in capturing both low- and high-level features within the same visual content, thus promoting the model for salient and subtle…
Sentiment analysis is rapidly advancing by utilizing various data modalities (e.g., text, video, and audio). However, most existing techniques only learn the atomic-level features that reflect strong correlations, while ignoring more complex compositions in multimodal data. Moreover, they also negle…
Visual Grounding (VG) aims to locate the most relevant object or region in an image according to a natural language query. Existing methods in VG utilize fixed image and text representations to capture cross-modal semantic consistency, which limits the flexibility in adjusting image representations…
Recently, Visual Question Answering(VQA), which is required to generate the answer by understanding both visual and textual content, has attracted considerable research interest. Most existing works extract visual features with the CNN network and learn its feature embedding with an attention mechan…
Generative Adversarial Network (GAN) has attracted rising attention for video future sequence prediction in driving scenes. However, the images generated by GAN often miss the target for lack of any constraints for its generated target. In this paper, an encoder-decoder based multi-task video predic…