arXiv Computer Vision
Sep 7

Cross-Domain Tracker Adaptation Without Target-Domain Labels via Vision-Language Agents

The paper introduces a Vision‑Language Model (VLM) that acts as a diagnostic agent to adapt a detect‑to‑track system to new domains without target‑domain labels. By inspecting rendered tracking outputs, the VLM identifies failure modes and iteratively recommends parameter updates, recovering a significant portion of performance lost when transferring hyperparameters from a source domain. Experiments on MOT17→MOT20 show the VLM tuner restores 67.8% of the lost headroom, while Bayesian optimization with proxy objectives performs poorly under large domain shifts.

By Daniel Davila, Ravikumar Balakrishnan, Mike Cochran
arXiv AI
Aug 21

Active Spiking Perception: The Membrane Potential as a Belief State for Anytime 3D Point Cloud Recognition

arXiv:2608. 19232v1 Announce Type: cross Abstract: Spiking point cloud networks usually scan space in a fixed, input-agnostic order, which leaves the most distinctive resource of spiking computation, the temporal evolution of the membrane potential, unused as a locus of decision-making.

By Akarsh Jain, Arya Pawa, Ayush Debnath, Smera Rawal, Sayeed Shafayet Chowdhury