Language Prompt vs. Image Enhancement: Boosting Object Detection With CLIP in Hazy Environments

• URL: http://arxiv.org/abs/2604.10637v1
• Date: Sun, 12 Apr 2026 13:26:59 GMT
• Title: Language Prompt vs. Image Enhancement: Boosting Object Detection With CLIP in Hazy Environments
• Abstract summary: Object detection in hazy environments is challenging because degraded objects are nearly invisible and their semantics are weakened by environmental noise.<n>Common approaches involve image enhancement to boost weakened semantics, but these methods are limited by the instability of enhanced modules.<n>This paper proposes a novel solution by employing language prompts to enhance weakened semantics without image enhancement.
• Score: 40.7433935066561
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detection in hazy environments is challenging because degraded objects are nearly invisible and their semantics are weakened by environmental noise, making it difficult for detectors to identify. Common approaches involve image enhancement to boost weakened semantics, but these methods are limited by the instability of enhanced modules. This paper proposes a novel solution by employing language prompts to enhance weakened semantics without image enhancement. Specifically, we design Approximation of Mutual Exclusion (AME) to provide credible weights for Cross-Entropy Loss, resulting in CLIP-guided Cross-Entropy Loss (CLIP-CE). The provided weights assess the semantic weakening of objects. Through the backpropagation of CLIP-CE, weakened semantics are enhanced, making degraded objects easier to detect. In addition, we present Fine-tuned AME (FAME) which adaptively fine-tunes the weight of AME based on the predicted confidence. The proposed FAME compensates for the imbalanced optimization in AME. Furthermore, we present HazyCOCO, a large-scale synthetic hazy dataset comprising 61258 images. Experimental results demonstrate that our method achieves state-of-the-art performance. The code and dataset will be released.

Related papers

• Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification [54.952203312050564]
  Adversarial attacks pose a severe threat to the reliability of deep learning models in remote sensing (RS) image classification.<n>We propose FogFool, a physically plausible adversarial framework that generates fog-based perturbations.<n>Experiments on two benchmark RS datasets demonstrate that FogFool achieves superior performance.
  arXiv  Detail & Related papers  (2026-04-16T05:47:57Z)
• When Semantics Regulate: Rethinking Patch Shuffle and Internal Bias for Generated Image Detection with CLIP [13.360123625878733]
  CLIP-based detectors often rely on semantic cues rather than generator artifacts, leading to brittle performance under distribution shifts.<n>We show that Patch Shuffle provides an unusually strong benefit for CLIP, that disrupts global semantic continuity.<n>We propose SemAnti, a semantic-antagonistic fine-tuning paradigm that freezes the semantic subspace and adapts only artifact-sensitive layers.
  arXiv  Detail & Related papers  (2025-11-24T13:54:00Z)
• DeeCLIP: A Robust and Generalizable Transformer-Based Framework for Detecting AI-Generated Images [14.448350657613368]
  DeeCLIP is a novel framework for detecting AI-generated images.<n>It incorporates DeeFuser, a fusion module that combines high-level and low-level features.<n>We trained exclusively on 4-class ProGAN data, DeeCLIP achieves an average accuracy of 89.90%.
  arXiv  Detail & Related papers  (2025-04-28T15:06:28Z)
• SeFENet: Robust Deep Homography Estimation via Semantic-Driven Feature Enhancement [14.1936051404987]
  Images captured in harsh environments often exhibit blurred details, reduced contrast, and color distortion.<n>We propose a semantic-driven feature enhancement network for robust homography estimation, dubbed SeFENet.<n>We show that SeFENet significantly outperforms SOTA methods, reducing point match error by at least 41% on the large-scale datasets.
  arXiv  Detail & Related papers  (2024-12-09T10:04:14Z)
• Source-Free Domain Adaptive Object Detection with Semantics Compensation [54.00183496587841]
  We introduce Weak-to-strong Semantics Compensation (WSCo) for strong data augmentation.<n>WSCo compensates for the class-relevant semantics that may be lost during strong augmentation on the fly.<n>WSCo can be implemented as a generic plug-in, easily integrable with any existing SFOD pipelines.
  arXiv  Detail & Related papers  (2024-10-07T23:32:06Z)
• Semantic Deep Hiding for Robust Unlearnable Examples [33.68037533119807]
  Unlearnable examples are proposed to mislead the deep learning models and prevent data from unauthorized exploration. We propose a Deep Hiding scheme that adaptively hides semantic images enriched with high-level features. Our proposed method exhibits outstanding robustness for unlearnable examples, demonstrating its efficacy in preventing unauthorized data exploitation.
  arXiv  Detail & Related papers  (2024-06-25T08:05:42Z)
• PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation [50.556961575275345]
  We propose a perception-aware fusion framework to promote segmentation robustness in adversarial scenes. We show that our scheme substantially enhances the robustness, with gains of 15.3% mIOU, compared with advanced competitors.
  arXiv  Detail & Related papers  (2023-08-08T01:55:44Z)
• CamDiff: Camouflage Image Augmentation via Diffusion Model [83.35960536063857]
  CamDiff is a novel approach to synthesize salient objects in camouflaged scenes. We leverage the latent diffusion model to synthesize salient objects in camouflaged scenes. Our approach enables flexible editing and efficient large-scale dataset generation at a low cost.
  arXiv  Detail & Related papers  (2023-04-11T19:37:47Z)
• Adversarial Semantic Data Augmentation for Human Pose Estimation [96.75411357541438]
  We propose Semantic Data Augmentation (SDA), a method that augments images by pasting segmented body parts with various semantic granularity. We also propose Adversarial Semantic Data Augmentation (ASDA), which exploits a generative network to dynamiclly predict tailored pasting configuration. State-of-the-art results are achieved on challenging benchmarks.
  arXiv  Detail & Related papers  (2020-08-03T07:56:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.