This paper presents a meta-synthesis that draws together four constituent studies covering adversarial machine learning, AI-powered anomaly detection in cloud environments, automated vulnerability patching by multi-agent large language model (LLM) pipelines, and the broader landscape of securing AI systems across their...
Harsh Verma· International Journal of Sci...· 0 citations
High-fidelity image-to-3D generation requires a 3D representation that captures both geometry and appearance. To support relighting and integration into standard rendering pipelines, the representation should include physically based rendering (PBR) modalities such as albedo, metallic-roughness, and surface normals. We...
M. Singh, Michele Stoppa, Alvise Memo et al.· 0 citations
The Agentic Video Auto-Encoder (AVA-Encoder), a novel auto-encoding framework driven by agentic self-evolution to learn agent-native video representations that outperforms a carefully human-tuned policy while using 74.3% fewer shot-level and keyframe-level system-prompt tokens.
Chuyue Li, Jinpeng Yu, Hao-Zhe Wang et al.· 1 citation
This paper proposes Scaffolding Minds, a dedicated scaffolding encoder that provides an optimized target in latent space, and learns both the mean and variance of the RL sampler, and shows that these two improvements are complementary, together yielding substantial gains over strong baselines.
Haoqiang Kang, Yin-Ping Chen, Lu-Yang Liu et al.· 2 citations
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Large vision-language models (LVLMs) have recently shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgments and interpretable defect reasoning. However, current LVLM-based IAD methods still struggle to produce precise pixel-level anomaly maps from generated language judgm...
Shuimu Chen, Jing Jin, Nan Su et al.· arXiv.org· 1 citation
This work presents a framework for quantizing VLMs for efficient inference on resource-constrained hardware, which combines a quantization pipeline that uses the model itself to generate training data and does not require access to the training setup, with a novel 2.7-bit-per-parameter format.
Luka Ribar, Jeevan Bhoot, Douglas Orr· 0 citations
This work formalizes target-mode coverage as target-mode coverage, the coverage of a predefined set of semantically specified modes, and proposes multi-axis max@K, a group-based reinforcement learning objective for improving it in diffusion-based T2I models.
Ku Onoda, Paavo Parmas, Hiroki Furuta et al.· arXiv.org· 3 citations
This work fusing lower-face video with facial electromyography from the occluded upper face to classify seven emotional categories, demonstrating that upper-face EMG provides robust complementary information under HMD-induced visual occlusion and establishing a foundation for multimodal emotion recognition in naturalis...
Birgit Nierula, Karam Tomotaki-Dawoud, M. Akguel et al.· 1 citation
Chehre, an emoji-prompted video dataset with a wide range of dynamic facial expressions for exploring perceptual variation, finds that persona prompting can act as a controllable way to shift model perception while helping models better capture the variation observed across human annotators.
Bita Azari, Zoe Stanley, Avneet Batra et al.· 0 citations
Reinforcement learning with verifiable rewards (RLVR) improves vision-language models (VLMs) by optimizing outcome rewards derived from final answers. However, such outcome-only rewards do not tell the model which image regions justify an answer. For questions that require visual grounding, these rewards cannot disting...
Editable Visual Design is proposed, a new paradigm driven by a Coding Agent that achieves both refined aesthetics and production-grade editability and faithfully reproduces the creative and reasoning trajectory akin to that of professional human designers.
Jun-Yan Ye, Wei Liu, Dongzhi Jiang et al.· 0 citations
Paired SylFishBD interventions show no significant weak-blur effect, modest losses from stronger blur/gray masking, a larger white-mask artifact, and strong species dependence.
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
