13 citations · 38 across the 14 of their papers we have counts for
15 papers
Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark
Jinyuan Liu, Zihang Chen, Zhu Liu +4
We engage in the relatively underexplored task named thermal infrared image enhancement. Existing infrared image enhancement methods primarily focus on tackling individual degradat…
HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning
Zengxi Zhang, Zhiying Jiang, Long Ma +3
Underwater images are often affected by light refraction and absorption, reducing visibility and interfering with subsequent applications. Existing underwater image enhancement met…
Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks
Zhiying Jiang, Xingyuan Li, Jinyuan Liu +2
Image stitching seamlessly integrates images captured from varying perspectives into a single wide field-of-view image. Such integration not only broadens the captured scene but al…
From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion
Xingyuan Li, Yang Zou, Jinyuan Liu +4
With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks. Despite its popularity, the inher…
Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction
Xiaoke Shang, Gehui Li, Zhiying Jiang +3
The correction of exposure-related issues is a pivotal component in enhancing the quality of images, offering substantial implications for various computer vision tasks. Historical…
CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception
Zengxi Zhang, Zeru Shi, Zhiying Jiang +1
Due to the uneven absorption of different light wavelengths in aquatic environments, underwater images suffer from low visibility and clear color deviations. With the advancement o…
Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction
Gehui Li, Jinyuan Liu, Long Ma +3
Photographs taken with less-than-ideal exposure settings often display poor visual quality. Since the correction procedures vary significantly, it is difficult for a single neural…
WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond
Zengxi Zhang, Zhiying Jiang, Jinyuan Liu +2
Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications. Existing underwater image enhancement methods ma…
Multi-Spectral Image Stitching via Spatial Graph Reasoning
Zhiying Jiang, Zengxi Zhang, Jinyuan Liu +2
Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view (FOV) scene. The primary chall…
Contrastive Learning Based Recursive Dynamic Multi-Scale Network for Image Deraining
Zhiying Jiang, Risheng Liu, Shuzhou Yang +2
Rain streaks significantly decrease the visibility of captured images and are also a stumbling block that restricts the performance of subsequent computer vision applications. The…
Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration
Zhiying Jiang, Zengxi Zhang, Jinyuan Liu +2
Since the differences in viewing range, resolution and relative position, the multi-modality sensing module composed of infrared and visible cameras needs to be registered so as to…
A Theory of Human-Like Few-Shot Learning
Zhiying Jiang, Rui Wang, Dongbo Bu +1
We aim to bridge the gap between our common-sense few-sample human learning and large-data machine learning. We derive a theory of human-like few-shot learning from von-Neuman-Land…
What the DAAM: Interpreting Stable Diffusion Using Cross Attention
Raphael Tang, Linqing Liu, Akshat Pandey +6
Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses. In this p…
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers
Ji Xin, Raphael Tang, Zhiying Jiang +2
There exists a wide variety of efficiency methods for natural language processing (NLP) tasks, such as pruning, distillation, dynamic inference, quantization, etc. We can consider…
Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond
Risheng Liu, Xin Fan, Minjun Hou +3
Single image dehazing is an important low-level vision task with many applications. Early researches have investigated different kinds of visual priors to address this problem. How…