World PulseNow
Newsletter

On Thin Ice: Towards Explainable Conservation Monitoring via Attribution and Perturbations

arXiv — cs.CV•Monday, October 27, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
A recent study highlights the potential of computer vision in enhancing ecological research and conservation monitoring, particularly through the use of explainable models. By applying post-hoc explanations to neural network predictions, researchers aim to build trust and address concerns about the reliability of these technologies in the field. This approach, demonstrated with aerial imagery from Glacier Bay National Park, could significantly improve the monitoring of pinnipeds and other wildlife, making conservation efforts more effective and transparent.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Continue Readings
Automatic Uncertainty-Aware Synthetic Data Bootstrapping for Historical Map Segmentation
arXiv — cs.CV2 days ago
Automatic Uncertainty-Aware Synthetic Data Bootstrapping for Historical Map Segmentation
PositiveArtificial Intelligence
The automated analysis of historical maps has significantly improved due to advancements in deep learning, particularly in computer vision. However, the scarcity of annotated training data for specific historical map corpora poses a challenge. To address this, a method for generating synthetic historical maps by transferring the cartographic style of original maps onto vector data has been proposed, enabling the creation of an unlimited number of training samples for machine learning tasks.
Read full article
via arXiv — cs.CV
Attention-Based Feature Online Conformal Prediction for Time Series
arXiv — cs.LG2 days ago
Attention-Based Feature Online Conformal Prediction for Time Series
PositiveArtificial Intelligence
The paper presents Attention-Based Feature Online Conformal Prediction (AFOCP) for time series analysis, enhancing online conformal prediction (OCP) by addressing limitations in output space and historical observation treatment. AFOCP utilizes feature space from pre-trained neural networks and incorporates an attention mechanism to adaptively weight historical data, improving prediction accuracy amidst non-stationarity and distribution shifts.
Read full article
via arXiv — cs.LG
Interpreting Emergent Features in Deep Learning-based Side-channel Analysis
arXiv — cs.LG2 days ago
Interpreting Emergent Features in Deep Learning-based Side-channel Analysis
PositiveArtificial Intelligence
Side-channel analysis (SCA) is a significant threat that exploits unintentional physical signals to extract confidential information from secure devices. Recent advancements in deep learning have improved SCA techniques, enhancing attack performance but reducing interpretability. This study applies mechanistic interpretability to neural networks used in SCA, revealing how models exploit specific leakage in side-channel traces, thereby aiding security evaluators in developing effective countermeasures.
Read full article
via arXiv — cs.LG
BioBench: A Blueprint to Move Beyond ImageNet for Scientific ML Benchmarks
arXiv — cs.CV2 days ago
BioBench: A Blueprint to Move Beyond ImageNet for Scientific ML Benchmarks
PositiveArtificial Intelligence
BioBench is introduced as an open ecology vision benchmark that addresses the limitations of ImageNet in predicting performance on scientific imagery. It encompasses 9 application-driven tasks, 4 taxonomic kingdoms, and 6 acquisition modalities, totaling 3.1 million images. The benchmark aims to enhance ecological research by providing a unified platform for evaluating visual representation quality in ecological tasks.
Read full article
via arXiv — cs.CV
Unified all-atom molecule generation with neural fields
arXiv — cs.LG2 days ago
Unified all-atom molecule generation with neural fields
PositiveArtificial Intelligence
FuncBind is a new framework designed for structure-based drug design that utilizes neural fields to generate target-conditioned, all-atom molecules. This approach allows for a unified model capable of handling diverse atomic systems, including small and large molecules, and non-canonical amino acids. FuncBind demonstrates competitive performance in generating various molecular structures, including small molecules and macrocyclic peptides, conditioned on target structures.
Read full article
via arXiv — cs.LG
TS-PEFT: Token-Selective Parameter-Efficient Fine-Tuning with Learnable Threshold Gating
arXiv — cs.CL2 days ago
TS-PEFT: Token-Selective Parameter-Efficient Fine-Tuning with Learnable Threshold Gating
PositiveArtificial Intelligence
The paper introduces Token-Selective Parameter-Efficient Fine-Tuning (TS-PEFT), a novel approach in natural language processing and computer vision that selectively applies modifications to a subset of position indices. This method challenges the traditional Parameter-Efficient Fine-Tuning (PEFT) approach, which indiscriminately modifies all indices. Experimental results indicate that the targeted application of TS-PEFT can enhance performance on downstream tasks, suggesting a shift towards more efficient fine-tuning strategies.
Read full article
via arXiv — cs.CL
Enhancing Visual Feature Attribution via Weighted Integrated Gradients
arXiv — stat.ML2 days ago
Enhancing Visual Feature Attribution via Weighted Integrated Gradients
PositiveArtificial Intelligence
The paper introduces Weighted Integrated Gradients (WG), an advanced method for feature attribution in explainable AI, particularly in computer vision. WG addresses the limitations of Integrated Gradients (IG) by adaptively selecting and weighting baseline images, improving attribution reliability. This method preserves the core properties of IG while enhancing the quality of explanations, making it a significant contribution to the field.
Read full article
via arXiv — stat.ML