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Found 16 papers, 9 papers with code
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
Large language models (LLMs) frequently hallucinate and produce factual errors, yet our understanding of why they make these errors remains limited.
Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration Network
Both the proposed JARNet and LAP image synthesis pipeline establish a foundation for addressing this intricate challenge.
FELM: Benchmarking Factuality Evaluation of Large Language Models
In this benchmark, we collect responses generated from LLMs and annotate factuality labels in a fine-grained manner.
Revealing the preference for correcting separated aberrations in joint optic-image design
The joint design of the optical system and the downstream algorithm is a challenging and promising task.
FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios
With the above challenges in mind, in this paper, we propose FacTool, a task and domain agnostic framework for detecting factual errors of texts generated by large language models (e. g., ChatGPT).
Let Segment Anything Help Image Dehaze
Therefore, we propose a framework to integrate large-model prior into low-level computer vision tasks.
Toward Real Flare Removal: A Comprehensive Pipeline and A New Benchmark
The procedure is comprehensive, where the similarity of scattered flares and the symmetric effect of reflected ghosts are realized.
Composing Parameter-Efficient Modules with Arithmetic Operations
In this paper, we propose to compose these parameter-efficient modules through linear arithmetic operations in the weight space, thereby integrating different module capabilities.
Evaluating Factual Consistency of Summaries with Large Language Models
Detecting factual errors in summaries has been an important and challenging subject in summarization research.
Mobile Image Restoration via Prior Quantization
In digital images, the performance of optical aberration is a multivariate degradation, where the spectral of the scene, the lens imperfections, and the field of view together contribute to the results.
Computational Optics for Mobile Terminals in Mass Production
Correcting the optical aberrations and the manufacturing deviations of cameras is a challenging task.
Optical Aberration Correction in Postprocessing using Imaging Simulation
Due to the spatial variation in optical aberrations, which cannot be avoided during the lens design process, recent commercial cameras have shifted some of these correction tasks from optical design to postprocessing systems.
Reliable Image Dehazing by NeRF
In order to obtain real shot data in different scenes, we used fog generators, array cameras, mobile phones, underwater cameras and drones to obtain haze data.
Non-blind optical degradation correction via frequency self-adaptive and finetune tactics
In mobile photography applications, limited volume constraints the diversity of optical design.
Adaptive Adversarial Training for Meta Reinforcement Learning
Meta Reinforcement Learning (MRL) enables an agent to learn from a limited number of past trajectories and extrapolate to a new task.
Extreme-Quality Computational Imaging via Degradation Framework
To meet the space limitation of optical elements, free-form surfaces or high-order aspherical lenses are adopted in mobile cameras to compress volume.
