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The iWildCam 2021 Competition Dataset

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arxiv 2105.03494 v1 pith:O6KMMLS3 submitted 2021-05-07 cs.CV

Sara Beery , Arushi Agarwal , Elijah Cole , Vighnesh Birodkar This is my paper · ORCID
classification cs.CV
keywords speciescameradataseentrapsacrosscameraschallenge
verification ladder T0 review T1 audit T2 compute T3 formal
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Camera traps enable the automatic collection of large quantities of image data. Ecologists use camera traps to monitor animal populations all over the world. In order to estimate the abundance of a species from camera trap data, ecologists need to know not just which species were seen, but also how many individuals of each species were seen. Object detection techniques can be used to find the number of individuals in each image. However, since camera traps collect images in motion-triggered bursts, simply adding up the number of detections over all frames is likely to lead to an incorrect estimate. Overcoming these obstacles may require incorporating spatio-temporal reasoning or individual re-identification in addition to traditional species detection and classification. We have prepared a challenge where the training data and test data are from different cameras spread across the globe. The set of species seen in each camera overlap, but are not identical. The challenge is to classify species and count individual animals across sequences in the test cameras.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AquaMonitor: A multimodal multi-view image sequence dataset for real-life aquatic invertebrate biodiversity monitoring

    cs.CV 2025-05 conditional novelty 7.0 of 10

    AquaMonitor is a 2.7M-image, multi-view, multimodal dataset of aquatic invertebrates collected during routine monitoring, with three benchmark tasks and baseline results.

  2. DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A hypernetwork generates per-column merging weights to combine source LoRA modules on CLIP, achieving state-of-the-art few-shot test-time domain adaptation.

  3. Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time

    cs.CV 2026-03 conditional novelty 6.0 of 10

    At a fixed camera-trap site, naively updating a recognition model on newly observed data frequently drops accuracy below the zero-shot baseline; LoRA with balanced softmax mostly fixes it, and post-processing closes m...

  4. Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctors

    cs.LG 2025-12 conditional novelty 5.0 of 10

    LMMs underperform few-shot experts on species recognition, but re-ranking the expert's top-5 candidates with an LMM improves mean accuracy by 6.4 points across five benchmarks.

  5. A model-agnostic active learning approach for animal detection from camera traps

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A black-box active learning method selects 30% of camera-trap data and outperforms full-data training on the SAWIT benchmark in a single-run evaluation.

  6. Measuring Weak-to-Strong Legibility of Reasoning Models

    cs.MA 2026-03 unverdicted novelty 4.0 of 10

    Strong reasoning models need traces weaker models can digest; current efficiency metrics miss thoroughness and understate this weak-to-strong legibility requirement.

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