Search Results for author:
Found 8 papers, 7 papers with code
Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment
In particular, we introduce a quality-aware image-text alignment strategy to make CLIP generate representations that correlate with the inherent quality of the images.
Blind Image Quality Assessment
No-Reference Image Quality Assessment
+1
Perceptual Quality Improvement in Videoconferencing using Keyframes-based GAN
Given that, in this context, the speaker is typically in front of the camera and remains the same for the entire duration of the transmission, we can maintain a set of reference keyframes of the person from the higher-quality I-frames that are transmitted within the video stream and exploit them to guide the visual quality improvement; a novel aspect of this approach is the update policy that maintains and updates a compact and effective set of reference keyframes.
Restoration of Analog Videos Using Swin-UNet
In this paper, we present a system to restore analog videos of historical archives.
Ranked #2 on
Analog Video Restoration
on TAPE
Reference-based Restoration of Digitized Analog Videotapes
We design a transformer-based Swin-UNet network that exploits both neighboring and reference frames via our Multi-Reference Spatial Feature Fusion (MRSFF) blocks.
Ranked #1 on
Analog Video Restoration
on TAPE
ARNIQA: Learning Distortion Manifold for Image Quality Assessment
In this work, we propose a self-supervised approach named ARNIQA (leArning distoRtion maNifold for Image Quality Assessment) for modeling the image distortion manifold to obtain quality representations in an intrinsic manner.
Ranked #2 on
No-Reference Image Quality Assessment
on CSIQ
Blind Image Quality Assessment
No-Reference Image Quality Assessment
+1
Mapping Memes to Words for Multimodal Hateful Meme Classification
Multimodal image-text memes are prevalent on the internet, serving as a unique form of communication that combines visual and textual elements to convey humor, ideas, or emotions.
Ranked #1 on
Hateful Meme Classification
on HarMeme
ECO: Ensembling Context Optimization for Vision-Language Models
Among these, the CLIP model has shown remarkable capabilities for zero-shot transfer by matching an image and a custom textual prompt in its latent space.
Zero-Shot Composed Image Retrieval with Textual Inversion
Composed Image Retrieval (CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption that describes the difference between the two images.
