Search Results for author: Song Wen

Found 9 papers, 4 papers with code

Improving Compositional Text-to-image Generation with Large Vision-Language Models

no code implementations10 Oct 2023 Song Wen, Guian Fang, Renrui Zhang, Peng Gao, Hao Dong, Dimitris Metaxas

However, compositional text-to-image models frequently encounter difficulties in generating high-quality images that accurately align with input texts describing multiple objects, variable attributes, and intricate spatial relationships.

Attribute Text-to-Image Generation

Improving Tuning-Free Real Image Editing with Proximal Guidance

1 code implementation8 Jun 2023 Ligong Han, Song Wen, Qi Chen, Zhixing Zhang, Kunpeng Song, Mengwei Ren, Ruijiang Gao, Anastasis Stathopoulos, Xiaoxiao He, Yuxiao Chen, Di Liu, Qilong Zhangli, Jindong Jiang, Zhaoyang Xia, Akash Srivastava, Dimitris Metaxas

Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control.

Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks

2 code implementations CVPR 2023 Jierun Chen, Shiu-hong Kao, Hao He, Weipeng Zhuo, Song Wen, Chul-Ho Lee, S. -H. Gary Chan

To achieve faster networks, we revisit popular operators and demonstrate that such low FLOPS is mainly due to frequent memory access of the operators, especially the depthwise convolution.

Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty

no code implementations12 Jan 2021 Jierun Chen, Song Wen, S. -H. Gary Chan

In this paper, we propose and study Wild-JDD, a novel learning framework for joint demosaicking and denoising in the wild.

Demosaicking Denoising

Crowd Counting on Images with Scale Variation and Isolated Clusters

1 code implementation9 Sep 2019 Haoyue Bai, Song Wen, S. -H. Gary Chan

Designing a general crowd counting algorithm applicable to a wide range of crowd images is challenging, mainly due to the possibly large variation in object scales and the presence of many isolated small clusters.

Clustering Crowd Counting

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