248 citations · 710 across the 48 of their papers we have counts for
7 papers · 1 filter
UniProbe: A Learnable Token-Level Hallucination Detector for Large VLMs using Multi-Structural Internal Representations
Dvir Samuel, Guy Bar-Shalom, Fabrizio Frasca +4
Large Vision-Language Models (LVLMs) achieve impressive visual reasoning and dialogue capabilities, yet frequently hallucinate content unsupported by the visual input. Effective mi…
Can Modern Vision Models Understand the Difference Between an Object and a Look-alike?
Itay Cohen, Ethan Fetaya, Amir Rosenfeld
Recent advances in computer vision have yielded models with strong performance on recognition benchmarks; however, significant gaps remain in comparison to human perception. One su…
Questioning the Stability of Visual Question Answering
Amir Rosenfeld, Neta Glazer, Ethan Fetaya
Visual Language Models (VLMs) have achieved remarkable progress, yet their reliability under small, meaning-preserving input changes remains poorly understood. We present the first…
GD-VDM: Generated Depth for better Diffusion-based Video Generation
Ariel Lapid, Idan Achituve, Lior Bracha +1
The field of generative models has recently witnessed significant progress, with diffusion models showing remarkable performance in image generation. In light of this success, ther…
DisCLIP: Open-Vocabulary Referring Expression Generation
Lior Bracha, Eitan Shaar, Aviv Shamsian +2
Referring Expressions Generation (REG) aims to produce textual descriptions that unambiguously identifies specific objects within a visual scene. Traditionally, this has been achie…
Auxiliary Learning by Implicit Differentiation
Aviv Navon, Idan Achituve, Haggai Maron +2
Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Two main challenges arise in this multi-task learning s…
Human Pose Estimation using Deep Consensus Voting
Ita Lifshitz, Ethan Fetaya, Shimon Ullman
In this paper we consider the problem of human pose estimation from a single still image. We propose a novel approach where each location in the image votes for the position of eac…