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Found 16 papers, 9 papers with code
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution
To this end, we engaged neural texture transfer to swap texture features between the low-resolution image and the high-resolution reference image.
SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution
Diffusion-based Video Super-Resolution (VSR) is renowned for generating perceptually realistic videos, yet it grapples with maintaining detail consistency across frames due to stochastic fluctuations.
Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
Seed-TTS offers superior controllability over various speech attributes such as emotion and is capable of generating highly expressive and diverse speech for speakers in the wild.
I$^2$VC: A Unified Framework for Intra- & Inter-frame Video Compression
Previous neural video compression methods necessitate distinct codecs for three types of frames (I-frame, P-frame and B-frame), which hinders a unified approach and generalization across different video contexts.
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict
Furthermore, we construct RU22Fact, a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022 of 16K samples, each containing real-world claims, optimized evidence, and referenced explanation.
MatSAM: Efficient Extraction of Microstructures of Materials via Visual Large Model
Efficient and accurate extraction of microstructures in micrographs of materials is essential in process optimization and the exploration of structure-property relationships.
Spatial-Related Sensors Matters: 3D Human Motion Reconstruction Assisted with Textual Semantics
Leveraging wearable devices for motion reconstruction has emerged as an economical and viable technique.
Semantic Lens: Instance-Centric Semantic Alignment for Video Super-Resolution
As a critical clue of video super-resolution (VSR), inter-frame alignment significantly impacts overall performance.
IBVC: Interpolation-driven B-frame Video Compression
Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction.
SigVIC: Spatial Importance Guided Variable-Rate Image Compression
Variable-rate mechanism has improved the flexibility and efficiency of learning-based image compression that trains multiple models for different rate-distortion tradeoffs.
Temporal Consistency Learning of inter-frames for Video Super-Resolution
A spatio-temporal stability module is designed to learn the self-alignment from inter-frames.
JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation
Video frame interpolation (VFI) aims to generate predictive frames by warping learnable motions from the bidirectional historical references.
Dense residual Transformer for image denoising
In this paper, we proposed an image denoising network structure based on Transformer, which is named DenSformer.
Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition
For the same action, the knowledge learned from different media types, e. g., videos and images, may be related and complementary.
Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition
However, the infrared action data is limited until now, which degrades the performance of infrared action recognition.
Global Temporal Representation based CNNs for Infrared Action Recognition
Existing methods for infrared action recognition are either based on spatial or local temporal information, however, the global temporal information, which can better describe the movements of body parts across the whole video, is not considered.
