arXiv:2607. 04709v1 Announce Type: new Abstract: Modern machine learning systems demand extensive datasets for visual recognition.
arXiv:2607. 00620v1 Announce Type: cross Abstract: Generalized Category Discovery (GCD) aims to recognize known classes while autonomously discovering novel ones in open-world settings.
The paper identifies a new inference-time issue called FGVR Laziness in Multimodal Large Language Models (MLLMs), where visual cues are insufficiently linked to category concepts during fine‑grained visual recognition. To address this, the authors propose LIVE, a training‑free framework that refines candidate lists and applies category‑specific guidance based on recognition uncertainty, thereby enhancing MLLM performance across 13 benchmarks and 24 state‑of‑the‑art methods. LIVE demonstrates improved effectiveness and generalizability across various MLLM backbones without requiring additional training.
Hidden‑Shot introduces an implicit prompt mechanism that extracts task‑specific visual information and merges it with in‑task processing to boost one‑shot performance on new low‑level vision tasks. The method injects this prompt cost‑effectively while minimally altering the base generalist model’s architecture. A data‑driven evaluation framework, C/U assessment, is proposed to systematically test generalization across conventional and unconventional tasks, and experiments on seven and ten datasets show Hidden‑Shot outperforming state‑of‑the‑art models.
arXiv:2605. 30170v2 Announce Type: replace-cross Abstract: While Large Vision-Language Models (VLMs) excel at interpolation, they suffer catastrophic failures in systematic generalization, most notably in visual counting.
arXiv:2606. 16193v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language tasks, yet their internal visual representations remain difficult to interpret.
arXiv:2606. 03569v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities but suffer from significant computational overhead during inference.
The paper proposes a data selection method that reallocates training samples across visual concepts such as objects, attributes, color, texture, and backgrounds. By using sparse autoencoders to extract concept activations and applying diminishing returns to concept support, the method down‑weights well‑represented concepts and focuses on the tail, while also incorporating local support to strengthen under‑supported concepts. Experiments across fine‑grained, spurious‑correlation, general image classification, and multimodal pretraining show consistent improvements over strong baselines and reveal a link between enhanced concepts and robustness to out‑of‑distribution and spurious contexts.
arXiv:2606. 00082v1 Announce Type: cross Abstract: Explainability of deep learning algorithms is critical for computer-vision applications with high-stake decisions.
Vision-Language Models (VLMs) have demonstrated remarkable capabilities but suffer from significant computational overhead during inference. While visual token pruning offers a promising solution, existing methods predominantly rely on initial attention scores.
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...
The paper introduces ClusterMixer, a token mixer based on hierarchical clustering in hyperbolic space, designed to be transparent and interpretable. It forms the core of a new vision backbone called HCFormer, which incorporates multiple clustering strategies to maintain strong performance. Experiments show HCFormer surpasses existing backbones on tasks such as image classification, object detection, instance segmentation, and semantic segmentation.
arXiv:2511. 10260v2 Announce Type: replace-cross Abstract: Fine-Grained Visual Classification (FGVC) remains a challenging task due to subtle inter-class differences and large intra-class variations.