LIRSeg is proposed, which fully replaces explicit CoT with a compact set of learnable latent tokens for reasoning segmentation, and introduces three complementary mechanisms from an information perspective: extreme-advantage sampling for selecting informative training signals, decoupled exploration-stability updates fo...
Tian-Hang Guo, Yu-Lin He, Wei Chen et al.· 0 citations
TrafficImag is introduced, the first benchmark for counterfactual roadside traffic video generation, which combines a large-scale roadside dataset with an executable protocol that supports behavior reasoning, intervention-aware image editing, and conditional video generation.
Xiang-Yu Li, Tian-Yi Wang, Zhi-Hao Dou et al.· 0 citations
VLALight is proposed, a lightweight end-to-end vision-language-action framework that directly maps intersection observations and signal-phase information to discrete signal actions and enables direct action prediction with a compact 0.5 B-parameter model.
Ke-Mou Jiang, Mao-Nan Wang, Xingchen Zou et al.· 0 citations
A key challenge in medical image analysis is the scarcity of large annotated datasets for specific populations and diseases. As deep learning models rely heavily on labeled data, effective transfer learning strategies are needed to reduce the dependence on manual annotations. Self-supervised learning has emerged as a p...
Tasneem Nasser, Susanne Schmid, Roberto Souza et al.· 0 citations
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This work proposes Source-Anchored Guidance via Frequency Equalization for Hierarchical RGB-T Alignment and Fusion (SAGE), a unified framework integrating frequency equalization, hierarchical alignment, and subband fusion.
Ti-Ming Li, Yi-Ming Sun, Bo-An Tao et al.· 0 citations
Dermoscopy classifiers built on Vision Transformers process all image patches uniformly, although diagnostic evidence is concentrated in the lesion region. Existing token pruning methods reduce tokens using generic saliency or similarity signals, but rarely ask whether the retained subset still contains the lesion. Thi...
Synchronised action annotations are needed to train controllable world models and these datasets remain elusive. Existing approaches make use of instrumented platforms with calibrated sensors, costly manual annotation, or latent-action models which lack grounding. We instead turn ordinary unlabelled video into action-s...
Ashish Sundar, Tiankuo Hou, Zhong Fan et al.· 0 citations
This paper conducts a large-scale study of multimodal design choices for misinformation detection with over 3,375 experiments, distill practical guidance on which design choices help, when do they fail silently, and what aspects of the pipeline most strongly shape model behavior.
Overall, persona prompting affects interpretive framing more strongly than descriptive grounding, and profile-pair similarity patterns are strongly correlated for all three output types, although agreement is lowest for justifications.
Neemias B. da Silva, Matt Ratto, Myriam Delgado et al.· 0 citations
This work introduces SVGLM, a framework that uses scalable vector graphics (SVG) primitives to connect text and image in reasoning tasks, and exploits the duality of SVG as both image description and text instructions, yielding a more compact, interpretable solution to equip general VLMs with the capability of generati...
Sun-Li Chen, Ding Zhong, Ziqiao Ma et al.· 0 citations
STRAND is introduced, a benchmark of human-verified object-centric facts that evaluates intermediate reasoning by decomposing queries into sub-questions, distinguishing genuine temporal understanding from coincidental correctness and an object-centric framework that explicitly constructs and reasons over structured obj...
T. Nguyen, C. Trinh, Khoi M. Le et al.· 0 citations
An embodied scene question-answering (QA) interface in which vision never enters the language model is studied, showing the gain survives paraphrase, attributing it to decision-aligned computation rather than answer-string leakage, while novel judgment vocabularies bound its scope.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
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.
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