Search Results for author:
Found 26 papers, 19 papers with code
TransVFC: A Transformable Video Feature Compression Framework for Machines
Then, the codec implements perception-guided conditional coding to minimize spatial redundancy and help the reconstructed features align with downstream machine perception. After that, the reconstructed features are transferred to new feature spaces for diverse downstream tasks by FST modules.
Prompt to Restore, Restore to Prompt: Cyclic Prompting for Universal Adverse Weather Removal
Universal adverse weather removal (UAWR) seeks to address various weather degradations within a unified framework.
PSVMA+: Exploring Multi-granularity Semantic-visual Adaption for Generalized Zero-shot Learning
Generalized zero-shot learning (GZSL) endeavors to identify the unseen categories using knowledge from the seen domain, necessitating the intrinsic interactions between the visual features and attribute semantic features.
Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning
Zero-shot learning (ZSL) endeavors to transfer knowledge from seen categories to recognize unseen categories, which mostly relies on the semantic-visual interactions between image and attribute tokens.
Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaption
Although recent generative image compression methods have demonstrated impressive potential in optimizing the rate-distortion-perception trade-off, they still face the critical challenge of flexible rate adaption to diverse compression necessities and scenarios.
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution
BlindDiff seamlessly integrates the MAP-based optimization into DMs, which constructs a joint distribution of the low-resolution (LR) observation, high-resolution (HR) data, and degradation kernels for the data and kernel priors, and solves the blind SR problem by unfolding MAP approach along with the reverse process.
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation
Inspired by the low-density separation assumption in semi-supervised learning, our key insight is that feature density can shed a light on the most promising direction for the segmentation classifier to explore, which is the regions with lower density.
Region-Adaptive Transform with Segmentation Prior for Image Compression
However, there is no prior research on neural transform that focuses on specific regions.
Ranked #1 on
Image Compression
on kodak
You Can Mask More For Extremely Low-Bitrate Image Compression
Extensive experiments have demonstrated that our approach outperforms recent state-of-the-art methods in R-D performance, visual quality, and downstream applications, at very low bitrates.
Exploring Resolution Fields for Scalable Image Compression with Uncertainty Guidance
In this work, we explore the potential of resolution fields in scalable image compression and propose the reciprocal pyramid network (RPN) that fulfills the need for more adaptable and versatile compression.
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information.
Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution
In this paper, we analyze the degradation of a high-resolution (HR) image from image intrinsic components according to a degradation-based formulation model.
Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
Recent blind SR methods suggest to reconstruct SR images relying on blur kernel estimation.
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline
Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks.
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration
In this paper, we present a deep interleaved network (DIN) that learns how information at different states should be combined for high-quality (HQ) images reconstruction.
Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-Attention
Recently, Convolutional Neural Networks (CNN) based image super-resolution (SR) have shown significant success in the literature.
Deep Optimized Multiple Description Image Coding via Scalar Quantization Learning
In this paper, we introduce a deep multiple description coding (MDC) framework optimized by minimizing multiple description (MD) compressive loss.
Concurrently Extrapolating and Interpolating Networks for Continuous Model Generation
Most deep image smoothing operators are always trained repetitively when different explicit structure-texture pairs are employed as label images for each algorithm configured with different parameters.
Deep Multiple Description Coding by Learning Scalar Quantization
Secondly, two entropy estimation networks are learned to estimate the informative amounts of the quantized tensors, which can further supervise the learning of multiple description encoder network to represent the input image delicately.
Virtual Codec Supervised Re-Sampling Network for Image Compression
In order to train RSN network and IDN network together in an end-to-end fashion, our VCN network intimates projection from the re-sampled vectors to the IDN-decoded image.
Mixed-Resolution Image Representation and Compression with Convolutional Neural Networks
Firstly, given one input image, feature description neural network (FDNN) is used to generate a new representation of this image, so that this image representation can be more efficiently compressed by standard codec, as compared to the input image.
Multiple Description Convolutional Neural Networks for Image Compression
Thirdly, multiple description virtual codec network (MDVCN) is proposed to bridge the gap between MDGN network and MDRN network in order to train an end-to-end MDC framework.
Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image
Due to the challenge of directly learning a non-linear function for a standard codec based on convolutional neural network, we propose to learn a virtual codec neural network to approximate the projection from the valid description image to the post-processed compressed image, so that the gradient could be efficiently back-propagated from the post-processing neural network to the feature description neural network during training.
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network
Firstly, given the low-resolution depth image and low-resolution color image, a generative network is proposed to leverage mutual information of color image and depth image to enhance each other in consideration of the geometry structural dependency of color-depth image in the same scene.
Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing
Both frameworks employ the division of gradient and the local activity measurement to achieve noise removal.
