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ADGym: Design Choices for Deep Anomaly Detection
Many WSAD methods attribute their improvements to novel network architectures or loss functions, based on the authors' understanding of
SCube: Instant Large-Scale Scene Reconstruction using VoxSplats
We present SCube, a novel method for reconstructing large-scale 3D scenes (geometry, appearance, and semantics) from a sparse set of posed images.
From Trainable Negative Depth to Edge Heterophily in Graphs Y uchen Y an
Upon increasing the depth, the over-smoothing issue arises: a GCN's
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
Our study aims to promote a higher standard of empirical rigor in the field of graph machine learning, encouraging more accurate comparisons and evaluations of model capabilities.
Boosting Adversarial Transferability by Achieving Flat Local Maxima
Specifically, we randomly sample an example and adopt a first-order procedure to approximate the Hessian/vector product, which makes computing more efficient by interpolating two neighboring gradients.
Large Language Models are Visual Reasoning Coordinators Liangyu Chen,, Bo Li
Existing methods like ensemble still struggle to aggregate these models with the desired higher-order communications. In this work, we propose Cola, a novel paradigm that coordinates multiple VLMs for visual reasoning.
OV-PARTS: Towards Open-Vocabulary Part Segmentation
OV -P ARTS includes refined versions of two publicly available datasets: Pascal-Part-116 and ADE20K-Part-234.