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Found 12 papers, 5 papers with code
SeD: Semantic-Aware Discriminator for Image Super-Resolution
In particular, one discriminator is utilized to enable the SR network to learn the distribution of real-world high-quality images in an adversarial training manner.
MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation
We present MicroCinema, a straightforward yet effective framework for high-quality and coherent text-to-video generation.
ART$\boldsymbol{\cdot}$V: Auto-Regressive Text-to-Video Generation with Diffusion Models
Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications.
CCEdit: Creative and Controllable Video Editing via Diffusion Models
The versatility of our framework is demonstrated through a diverse range of choices in both structure representations and personalized T2I models, as well as the option to provide the edited key frame.
Semantically Structured Image Compression via Irregular Group-Based Decoupling
Nevertheless, they divide the input image into multiple rectangular regions according to semantics and ignore avoiding information interaction among them, causing waste of bitrate and distorted reconstruction of region boundaries.
Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts
For ICM, developing a unified codec to reduce information redundancy while empowering the compressed features to support various vision tasks is very important, which inevitably faces two core challenges: 1) How should the compression strategy be adjusted based on the downstream tasks?
Inpaint Anything: Segment Anything Meets Image Inpainting
We are also very willing to help everyone share and promote new projects based on our Inpaint Anything (IA).
HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and low-resolution artifacts.
Ranked #1 on
Compressed Image Super-resolution
on DIV2K-q40-x4
Image Coding for Machines with Omnipotent Feature Learning
Learning a kind of feature that is both general (for AI tasks) and compact (for compression) is pivotal for its success.
Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks
Traditional media coding schemes typically encode image/video into a semantic-unknown binary stream, which fails to directly support downstream intelligent tasks at the bitstream level.
Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization
Specifically, we introduce Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations by leveraging personal unique and cloth-independent gait information, we name this framework as GI-ReID.
Ranked #5 on
Person Re-Identification
on PRCC
Cloth-Changing Person Re-Identification
Computational Efficiency
+1
Local Patch AutoAugment with Multi-Agent Collaboration
We formulate it as a multi-agent reinforcement learning (MARL) problem, where each agent learns an augmentation policy for each patch based on its content together with the semantics of the whole image.
