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20232026
most citedSet Features for Fine-grained Anomaly Detection

12 citations · 16 across the 10 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Stuck on Shuffle: On Attribute-Object Binding in Vision-Language Models

Netanel Y. Tamir, Michael Green, Issar Tzachor +4

Retrieval is a fundamental task in multimodal systems: given an image or text query, the goal is to find the most relevant images or texts in a collection, often using a shared emb…

cs.CV2026

WarpHammer: Densifying Scene Warps with 3D Object Priors for Extreme View Synthesis

Michael Green, Gavriel Habib, Dvir Samuel +4

Projection-conditioned novel view synthesis (NVS) warps an explicit 3D reconstruction of the input view into the target camera and conditions a generator on the warped rendering. T…

cs.CV2026

VidMsg: A Benchmark for Implicit Message Inference in Short Videos

Issar Tzachor, Michael Green, Rami Ben-Ari

Understanding short online videos involves more than identifying visible objects and actions; video makers often include an underlying message or purpose in the clip. We introduce…

cs.CV2026

Retrieval-Augmented Gaussian Avatars: Improving Expression Generalization

Matan Levy, Gavriel Habib, Issar Tzachor +5

Template-free animatable head avatars can achieve high visual fidelity by learning expression-dependent facial deformation directly from a subject's capture, avoiding parametric fa…

cs.CV2026

VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval

Issar Tzachor, Dvir Samuel, Rami Ben-Ari

Recent studies have adapted generative Multimodal Large Language Models (MLLMs) into embedding extractors for vision tasks, typically through fine-tuning to produce universal repre…

cs.CV2026

Fast Autoregressive Video Diffusion and World Models with Temporal Cache Compression and Sparse Attention

Dvir Samuel, Issar Tzachor, Matan Levy +3

Autoregressive video diffusion models enable streaming generation, opening the door to long-form synthesis, video world models, and interactive neural game engines. However, their…