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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arXiv AI
Sep 1
Making the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light
arXiv:2605.22455v2 Announce Type: replace-cross Abstract: Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse...
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 Computer Vision
Sep 7
Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods
The paper introduces MovingDroneCrowd++, a large-scale video dataset for dense crowd counting and tracking from moving drones, featuring varied flight altitudes, camera angles, and lighting. It presents two new methods: GD3A for Video Individual Counting and GIA-Track for Multi-Object Tracking, both leveraging group-wise density assignment and identity association to handle aerial challenges. Experiments demonstrate significant improvements, reducing counting error by 47.4% and boosting tracking accuracy by 64.6%.
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
Sep 10
RelightFormer: Feed-forward Generative Transformer for Multiview Object Relighting
RelightFormer is a feed‑forward generative Transformer that performs single‑ and multi‑view image relighting without explicit intrinsic property estimation. It incorporates a latent illumination module that injects target environment maps into spatial features via cross‑attention, and uses permutation‑invariant positional encodings to process unordered multi‑view inputs symmetrically. Trained on the large Laval Objaverse Dataset, the model achieves state‑of‑the‑art visual and photorealistic relighting quality, and demonstrates strong zero‑shot generalization across various relighting tasks.
arXiv Computer Vision
Sep 22
CrowdCue: Specialist-Cue Conditioning for Vision-Language Crowd Counting
arXiv:2609.23012v1 Announce Type: new Abstract: Generative vision-language models (VLMs) offer a counting paradigm in which one model produces both a count and a natural-language account of the scene...