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Evidence record 17337 · automatically gathered

Depth-Guided Video Object Counting in Crowded Scenes

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. Existing methods rely on RGB information, limiting their discriminative ability in crowded and occluded conditions. To address this, we propose a Depth-Guided Detector (DG-Det) along with a general post-processing pipeline. By integrating depth cues with multi-scale RGB-D cross-attention and explicit occlusion prediction,

Record details

Published: 6 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Bias & fairness
Retrieved: 7 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness

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Shared topic: Bias & fairness

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Shared topic: Bias & fairness

Conditional Cognitive Biases in LLMs: How Biased User Turns Modulate In-Context Reasoning

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How to cite this record

ethics.ai (6 August 2026), “Depth-Guided Video Object Counting in Crowded Scenes,” evidence record 17337, https://ethics.ai/record/17337 (originally published by arXiv cs.AI).

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