The paper introduces a multimodal dog‑fingerprinting framework that combines visual identification, multiple‑object tracking, and canine vocal fingerprinting to associate bark events with individual dogs in real‑world videos. It synchronizes bark events with tracked dogs and updates persistent profiles using both visual appearance and vocal characteristics, allowing identification even when dogs are occluded, overlapping vocalizations occur, or dogs bark off‑screen. The authors also present a benchmark of 46 real‑world videos with 213 manually annotated bark events, reporting a 61.83% localization accuracy for visible dogs (IoU ≥ 0.30) and a 91.33% F1 score for detecting visible versus off‑screen barking.
Animal pose estimation and tracking is important for wildlife monitoring and conservation research, and with limited expert time for labelling automated approaches are imperative. While human pose estimation and tracking has seen rapid progress thanks to large annotated datasets, animal pose remain challenging, due to large morphological and behavioural differences between species and limited annotated data.
Human-pet interaction estimation and generation remain underexplored due to the absence of a high-quality large-scale dataset. We present InterPet4D, the first multimodal dataset capturing natural interactions between humans and dogs.
arXiv:2607. 02680v1 Announce Type: cross Abstract: MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text.
VastMAT is a large‑scale multi‑animal tracking benchmark featuring 2,947 videos, 337 animal categories, and over 3.6 million bounding boxes with 22,883 identity trajectories. It emphasizes high‑quality, expert‑reviewed annotations and introduces Seen‑category and Unseen‑category evaluation protocols, revealing significant challenges in tracking unseen animals. The authors also propose a lightweight Center‑Distance‑Augmented Association module that boosts HOTA scores for existing MOT methods without extra training.
arXiv:2609.15221v1 Announce Type: cross Abstract: Passive acoustic monitoring can measure biodiversity at larger scales, but time--frequency annotation of animal vocalizations is expensive, site-spec...
arXiv:2610.33127v1 Announce Type: cross Abstract: Active speaker detection (ASD) relies on audiovisual correspondence between speech and visible faces, yet this correspondence becomes unreliable in c...
arXiv:2610.08417v1 Announce Type: new Abstract: Recent advances in LipSync generation technology have led to the creation of highly realistic videos, posing severe societal risks. However, existing d...
Recent advances in LipSync generation technology have led to the creation of highly realistic videos, posing severe societal risks. However, existing defense strategies struggle against LipSync forger...
The paper presents MaxBoxCount, the winning solution to the iWildCam 2021 Challenge, which tackles counting animals in camera‑trap image sequences without using count labels. It combines a robust species classification pipeline with a counting heuristic based on MegaDetector detections to estimate the number of unique individuals across short image bursts. The method addresses challenges posed by temporal discontinuities and the high cost of manual count annotations.
arXiv:2503.13344v3 Announce Type: replace Abstract: Maintaining a consistent identity across video frames while estimating pose is crucial for downstream tasks such as behavior analysis and action re...
The paper introduces MetaWild, a multimodal animal re-identification dataset that pairs 20,890 images from six species with environmental metadata such as temperature and circadian rhythms. It also proposes the Meta-Feature Adapter (MFA), a lightweight module that integrates this metadata into vision-language models for re-identification tasks. Experiments demonstrate that adding MFA consistently improves re-identification performance over visual-only baselines.
The paper introduces AVAPrintDB, a new public multi‑generator talking‑head avatar database designed for avatar fingerprinting, comprising data from two audiovisual corpora and three state‑of‑the‑art generators (GAGAvatar, LivePortrait, HunyuanPortrait). It also defines a standardized benchmark that evaluates existing avatar fingerprinting systems and explores new methods based on Foundation Models such as DINOv2 and CLIP, while analyzing performance under generator and dataset shift. The authors find that identity‑related motion cues persist across synthetic avatars, yet current fingerprinting systems are highly sensitive to changes in synthesis pipelines and source domains.