arXiv AI
Jun 18
Multi-Modal Hyper-Graph Fusion for Low-Light Crowd Counting
arXiv:2606. 18566v1 Announce Type: cross Abstract: Crowd counting is a fundamental task in computer vision.
Multi-Modal Hyper-Graph Fusion for Low-Light Crowd Counting
Crowd counting is a fundamental task in computer vision. However, crowd counting in low-light environments remains largely underexplored, despite its practical importance in the real world.
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Hugging Face Trending Papers
Jun 22
LUMINA-26: Low-Light Understanding for Modeling and Interpreting Night-time Actions
Low-light human action recognition remains a challenging problem due to poor illumination, amplified noise, motion ambiguity, and diverse real-world scenes. Existing low-light datasets often lack sufficient action diversity, capture realism, or balanced class distribution, limiting the development of robust models.
arXiv AI
Jun 15
Pix2Pix-Hybrid: Structure-Guided Conditional Synthesis of Hajj Crowd Images with Multi-Channel Conditioning and Weak Attribute Supervision
arXiv:2606. 14297v1 Announce Type: cross Abstract: Developing accurate crowd-counting models for Hajj pilgrimage scenes remains challenging because domain-specific annotated images are scarce and data collection during large gatherings raises privacy concerns.
arXiv AI
Aug 7
Depth-Guided Video Object Counting in Crowded Scenes
arXiv:2608. 06236v1 Announce Type: cross Abstract: Our primary objective is to advance video object counting in crowded scenes, aiming to robustly count all instances of a target category based on given text or visual prompts.
arXiv AI
Jul 28
Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex
arXiv:2604. 10359v3 Announce Type: replace-cross Abstract: Low-light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation.
arXiv Machine Learning
Jun 8
Lighting-Aware Representation Learning under Controllable Lighting Variation
arXiv:2606. 06899v1 Announce Type: cross Abstract: Variations in illumination remain a major challenge for visual representation learning, as they induce substantial appearance changes both across and within environments.
Hugging Face Trending Papers
Jul 8
HAJJv2-CrowdCount: Zero-Shot Benchmark for Dense Crowd Counting
Automated crowd counting in Hajj video is difficult not because current models lack capacity, but because the footage violates the assumptions those models were built on: cameras observe the crowd from steep, near-vertical angles, individuals occlude one another extensively, and a single frame can contain well over a thousand people. Benchmarks that test crowd counting in such an environment are either private or not detailed per second.
arXiv AI
Aug 5
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
arXiv:2403. 09281v3 Announce Type: cross Abstract: We propose CLIP-EBC, the first fully CLIP-based model for accurate crowd density estimation.
arXiv AI
Jun 16
Seeing Roads Through Words: A Language-Guided Framework for RGB-T Driving Scene Segmentation
arXiv:2602. 07343v2 Announce Type: replace-cross Abstract: Robust semantic segmentation of road scenes under adverse illumination, lighting, and shadow conditions remain a core challenge for autonomous driving applications.
arXiv AI
Jul 7
MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model
arXiv:2607. 03013v1 Announce Type: cross Abstract: Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limitations and imaging pipelines, which degrades visual quality and affects downstream vision tasks.
arXiv AI
Jul 9
HAJJv2-CrowdCount: Zero-Shot Benchmark for Dense Crowd Counting
arXiv:2607. 07322v1 Announce Type: cross Abstract: Automated crowd counting in Hajj video is difficult not because current models lack capacity, but because the footage violates the assumptions those models were built on: cameras observe the crowd from steep, near-vertical angles, individuals occlude one another extensively, and a single frame can contain well over a thousand people.
arXiv AI
Jun 18
VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation
arXiv:2502. 07531v5 Announce Type: replace-cross Abstract: Controllable image-to-video (I2V) generation transforms a reference image into a coherent video guided by user-specified control signals.