CoreDNA is an unsupervised, task‑agnostic coreset selection method that uses neuron activations from vision foundation models to build per‑image and per‑dataset distributions. By comparing each image’s distribution to the overall dataset distribution, it captures both low‑level visual structure and high‑level semantic information across layers, identifying high‑quality samples in the tails of the last layer’s activation statistics. The method achieves state‑of‑the‑art performance across image classification, semantic segmentation, depth estimation, and imitation learning for driving and mobile manipulation, demonstrating broad generalization without task‑specific tailoring.
arXiv:2605.12491v2 Announce Type: replace Abstract: Vision Transformers (ViTs) learn rich visual-semantic representations through all-to-all self-attention among patch tokens. However, this design im...
arXiv:2605.05556v2 Announce Type: replace Abstract: Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that...
arXiv:2607. 16012v1 Announce Type: cross Abstract: Multi-Task Learning (MTL) in robotics perception systems supports comprehensive 3D spatial scene understanding by integrating semantic segmentation and depth estimation.
arXiv:2505.07675v3 Announce Type: replace-cross Abstract: Semi-supervised learning (SSL) has emerged as a practical solution for addressing data scarcity challenges by leveraging unlabeled data. Rece...
arXiv:2609.21522v1 Announce Type: new Abstract: Recent pre-trained foundation models provide rich multi-modal priors for downstream 3D vision tasks. However, the effectiveness of these representation...
arXiv:2605. 05627v2 Announce Type: replace-cross Abstract: Sustainable forest management relies on precise species composition mapping, yet traditional ground surveys are labour-intensive and geographically constrained.
arXiv:2610.17661v2 Announce Type: replace Abstract: The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic...
arXiv:2407.03463v2 Announce Type: replace-cross Abstract: In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets fo...
The paper introduces a method for selecting the best vision‑language model for a downstream task by analyzing the internal dynamics of the visual encoder. It represents each task with layer‑wise conductance and uses an entropy‑regularized alignment to derive a target‑conditioned block importance distribution. The proposed Directional Conductance Divergence (DCD) metric captures asymmetric transferability, enabling accurate prediction of model rankings without direct inference, and achieves a 14.7% NDCG@5 improvement over SWAB on 48 VLMs across 21 datasets.
arXiv:2606. 08156v1 Announce Type: cross Abstract: Vision Transformers (ViTs) achieve strong performance but suffer from high computational costs due to quadratic self-attention complexity.
UVU is a vision-language unified autoregressive framework that integrates visual supervision directly into the pre-training stage of multimodal large language models. By using continuous visual encoding and a large-scale iterative hierarchical clustering algorithm to build a pixel-level visual codebook, UVU enables lossless representation of visual inputs and autoregressive generation of pixel-level image tokens alongside textual tokens. This approach synergizes pixel-level visual perception with semantic-level visual understanding, allowing models to internalize visual reconstruction capabilities and improve multimodal understanding performance.
The paper introduces D2MoE, a Mixture of Task Adapters designed for vision foundation models to handle multi-task dense prediction. It separates adaptation into a task‑generic low‑rank channel (TG‑LoRA) that preserves pretrained priors and a task‑adaptive sparse expert module (TA‑MoE) that resolves task‑specific conflicts. A Semantic Prior Injector further aligns multi‑level task features with frozen foundation knowledge, achieving robust multi‑task performance with a small trainable footprint.