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Found 13 papers, 4 papers with code
Boosting Fair Classifier Generalization through Adaptive Priority Reweighing
Our adaptive reweighing method prioritizes samples closer to the decision boundary and assigns a higher weight to improve the generalizability of fair classifiers.
Inversion-by-Inversion: Exemplar-based Sketch-to-Photo Synthesis via Stochastic Differential Equations without Training
In the full-control inversion process, we propose an appearance-energy function to control the color and texture of the final generated photo. Importantly, our Inversion-by-Inversion pipeline is training-free and can accept different types of exemplars for color and texture control.
VideoControlNet: A Motion-Guided Video-to-Video Translation Framework by Using Diffusion Model with ControlNet
In this work, by using the diffusion model with ControlNet, we proposed a new motion-guided video-to-video translation framework called VideoControlNet to generate various videos based on the given prompts and the condition from the input video.
Complexity-Guided Slimmable Decoder for Efficient Deep Video Compression
In this work, we propose the complexity-guided slimmable decoder (cgSlimDecoder) in combination with skip-adaptive entropy coding (SaEC) for efficient deep video compression.
Content Adaptive Latents and Decoder for Neural Image Compression
Although several content adaptive methods have been proposed by updating the encoder-side components, the adaptability of both latents and the decoder is not well exploited.
Coarse-to-fine Deep Video Coding with Hyperprior-guided Mode Prediction
The previous deep video compression approaches only use the single scale motion compensation strategy and rarely adopt the mode prediction technique from the traditional standards like H. 264/H. 265 for both motion and residual compression.
LSVC: A Learning-Based Stereo Video Compression Framework
In this work, we propose the first end-to-end optimized framework for compressing automotive stereo videos (i. e., stereo videos from autonomous driving applications) from both left and right views.
FVC: A New Framework towards Deep Video Compression in Feature Space
In this work, we propose a feature-space video coding network (FVC) by performing all major operations (i. e., motion estimation, motion compression, motion compensation and residual compression) in the feature space.
StyleFormer: Real-Time Arbitrary Style Transfer via Parametric Style Composition
In this work, we propose a new feed-forward arbitrary style transfer method, referred to as StyleFormer, which can simultaneously fulfill fine-grained style diversity and semantic content coherency.
SAMOT: Switcher-Aware Multi-Object Tracking and Still Another MOT Measure
Besides, we propose a new MOT evaluation measure, Still Another IDF score (SAIDF), aiming to focus more on identity issues. This new measure may overcome some problems of the previous measures and provide a better insight for identity issues in MOT.
Improving Deep Video Compression by Resolution-adaptive Flow Coding
In this work, we propose a new framework called Resolution-adaptive Flow Coding (RaFC) to effectively compress the flow maps globally and locally, in which we use multi-resolution representations instead of single-resolution representations for both the input flow maps and the output motion features of the MV encoder.
A Unified End-to-End Framework for Efficient Deep Image Compression
Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance.
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
In this paper, we propose a unified Multi-Object Tracking (MOT) framework learning to make full use of long term and short term cues for handling complex cases in MOT scenes.
