arXiv AI

Adversarially Robust Abductive Fusion of Pre-trained Transformer-based Perception Models

arXiv:2608. 04190v1 Announce Type: new Abstract: Deploying pre-trained perception models in novel environments degrades their accuracy under distributional shift, and assembling them alone does not recover it: combiners such as majority voting trade recall for precision and are brittle to coordinated failures.

arXiv Machine Learning

Jul 14

Vertical Fusion: Condensing Internal Representations for Robust ViT Classification

arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.

By Francesco Di Salvo, Shyam Nandan Rai, Hamed Damirchi, Ignacio Meza De la Jara, Sebastian Doerrich, Marco Lents, Christian Ledig

llms diffusion

arXiv Computer Vision

Aug 25

Specialist-Generalist Fusion with Outcome-Supervised Rationales for Deepfake Detection

arXiv:2605.31192v2 Announce Type: replace Abstract: Generalizable deepfake detection requires complementary forensic and semantic visual evidence. Specialist encoders capture subtle manipulation trac...

By Benedikt Hopf, Zongwei Wu, Radu Timofte

robotics benchmarks safety

arXiv Computer Vision

2d ago

Subtoken Vision Transformer for Fine-grained Recognition

arXiv:2607.09086v2 Announce Type: replace Abstract: We present Subtoken Vision Transformer (SubViT), a selective image tokenization method for fine-grained visual recognition. Standard Vision Transfo...

By Jie Zhu, Ivy Zhang, Minchul Kim, Xiaoming Liu

llms computer-vision nlp

arXiv Machine Learning

Jun 4

Rethinking Incompleteness: Formalizing Protocol Divergence and Train-Once Learning for Robust IMVC

arXiv:2606. 04857v1 Announce Type: new Abstract: Standard IMVC evaluation retrains separate models for different missing-data configurations.

By Haolu Liu, Xiyue Wang, Xuanting Xie, Liangjian Wen, Zhao Kang

llms benchmarks

arXiv AI

Aug 5

In-Context Collapse in Vision-Language Models and How to Mitigate it?

arXiv:2608. 02830v1 Announce Type: cross Abstract: Many-shot in-context learning (ICL) lets vision-language models (VLMs) adapt from image--label demonstrations without weight updates, and is widely assumed to improve as more demonstrations are supplied.

By Mohammad Rostami

llms computer-vision multimodal benchmarks

arXiv AI

Jul 1

The Label Imitation Game: Turing Test Network for Zero-Shot Pseudo-Label Pruning

arXiv:2606. 30875v1 Announce Type: cross Abstract: Foundation model pseudo-labeling - labeling data strictly via zero-shot inference - enables massive scale, but performance is undermined by hallucinations that evade standard thresholds.

By Brent A. Griffin, Jason J. Corso

llms computer-vision fine-tuning efficiency multimodal benchmarks safety

arXiv Computer Vision

Sep 14

Retrieved Images as Visual Thought: Training-Free Multimodal In-Context Learning for the Open-vs-Closed Gap

ReVisIT is a train‑free framework that turns retrieved image‑label pairs into units of visual thought, combining structured class definitions, multimodal retrieval, and alternating user/assistant injection before joint decoding. On several benchmarks—including Fast Open MiniImageNet, Bongard‑OpenWorld, and the newly released MAAC‑Bench—ReVisIT achieves performance comparable to or surpassing large, trained models while using far fewer parameters. The approach demonstrates that high‑quality retrieval and a simple turns layer can provide a universal performance boost across diverse multimodal tasks.

By Bingchen Huang, Zhiling Wang, Yifu Chen, Yuanchao Du

llms multimodal benchmarks

arXiv AI

Jun 12

Bag of Dims: Training-Free Mechanistic Interpretability via Dimension-Level Sign Patterns

arXiv:2606. 12629v1 Announce Type: cross Abstract: We show that the standard basis of transformer hidden states already provides a training-free, architecture-general feature basis.

By Varun Reddy Nalagatla

llms efficiency safety

arXiv AI

Jul 1

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation

arXiv:2603. 23867v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts.

By Weixin Chen, Antonio Vergari, Han Zhao

llms fine-tuning multimodal

arXiv Computer Vision

Sep 17

Investigating Adversarial Robustness of Heterogeneous Cooperative Perception

arXiv:2609.17856v1 Announce Type: new Abstract: Heterogeneous cooperative perception (CP) enables connected vehicles with diverse sensor setups to share spatial awareness via compact feature maps, wh...

By Chenyi Wang, Yutong Liu, Qingzhao Zhang, Ming F. Li

agents safety

arXiv AI

Jul 1

Real-Time Source-Free Object Detection

arXiv:2606. 31834v1 Announce Type: cross Abstract: Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detection (SFOD) methods rely on heavyweight architectures that prioritize accuracy alone.

By Sairam VCR, Varun Gopal, Poornima Jain, Vineeth N Balasubramanian, Muhammad Haris Khan

agents computer-vision robotics efficiency benchmarks

arXiv Machine Learning

Jun 4

Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention

arXiv:2606. 04364v1 Announce Type: cross Abstract: Concept bottleneck models (CBMs) predict a layer of human-named attributes before predicting a class, which makes their decisions auditable.

By Dhanesh Ramachandram

llms computer-vision

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