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BiHashFormer: Hash-Driven Dual-Branch Transformer for Efficient Object Detection in HRW Shots

Paper recorded by Signals 4 on 2026-10-29 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.CV · 计算机视觉 · first seen 2026-10-30

Abstract

Recent advances in gigapixel-level imaging have brought High-Resolution Wide shots to the forefront of research. However, these images present significant challenges: extreme sparsity of foreground, gigapixel-level resolutions and interleaving of foreground and background. This causes traditional detectors and attention mechanisms to be hindered by background, resulting in inefficiency and inaccur

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#71 most recent of 710 cs.CV papers we have recorded · ↑ newer: ElasticFormer: Detecting Objects in HRW Shots via Elastic Computing Vi · ↓ older: MLLM-GS: Multimodal Large Language Model Prior-Guided Fast Novel View
Cite this page: BiHashFormer: Hash-Driven Dual-Branch Transformer for Efficient Object Detection in HRW Shots: the #71 most recent of 710 cs.CV papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/bihashformer-hash-driven-dual-branch-transformer-for-efficient-object-detection-.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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