4 citations · 9 across the 17 of their papers we have counts for
12 papers · 1 filter
Meta-Video: Efficient Standardized Evaluation for Video Understanding
Soumya Shamarao Jahagirdar, Anna Kukleva, Vishaal Udandarao +5
Video understanding benchmarks are the primary yardstick for measuring progress in video-language modeling, and a growing body of work relies on them to claim improvements. The rep…
AVRT: Audio-Visual Reasoning Transfer through Single-Modality Teachers
Edson Araujo, Saurabhchand Bhati, M. Jehanzeb Mirza +5
Recent advances in reasoning models have shown remarkable progress in text-based domains, but transferring those capabilities to multimodal settings, e.g., to allow reasoning over…
TTA-Vid: Generalized Test-Time Adaptation for Video Reasoning
Soumya Shamarao Jahagirdar, Edson Araujo, Anna Kukleva +7
Recent video reasoning models have shown strong results on temporal and multimodal understanding, yet they depend on large-scale supervised data and multi-stage training pipelines,…
TTRV: Test-Time Reinforcement Learning for Vision Language Models
Akshit Singh, Shyam Marjit, Wei Lin +7
Existing methods for extracting reward signals in Reinforcement Learning typically rely on labeled data and dedicated training splits, a setup that contrasts with how humans learn…
Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence
Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60
We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document un…
TimeLogic: A Temporal Logic Benchmark for Video QA
Sirnam Swetha, Hilde Kuehne, Mubarak Shah
Temporal logical understanding, a core facet of human cognition, plays a pivotal role in capturing complex sequential events and their temporal relationships within videos. This ca…
Teaching VLMs to Localize Specific Objects from In-context Examples
Sivan Doveh, Nimrod Shabtay, Wei Lin +9
Vision-Language Models (VLMs) have shown remarkable capabilities across diverse visual tasks, including image recognition, video understanding, and Visual Question Answering (VQA)…
LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity
Walid Bousselham, Angie Boggust, Sofian Chaybouti +2
Vision Transformers (ViTs), with their ability to model long-range dependencies through self-attention mechanisms, have become a standard architecture in computer vision. However,…
Grounding Everything: Emerging Localization Properties in Vision-Language Transformers
Walid Bousselham, Felix Petersen, Vittorio Ferrari +1
Vision-language foundation models have shown remarkable performance in various zero-shot settings such as image retrieval, classification, or captioning. But so far, those models s…
Fréchet Wavelet Distance: A Domain-Agnostic Metric for Image Generation
Lokesh Veeramacheneni, Moritz Wolter, Hildegard Kuehne +1
Modern metrics for generative learning like Fréchet Inception Distance (FID) and DINOv2-Fréchet Distance (FD-DINOv2) demonstrate impressive performance. However, they suffer from v…
Weakly Supervised Grounding for VQA in Vision-Language Transformers
Aisha Urooj Khan, Hilde Kuehne, Chuang Gan +2
Transformers for visual-language representation learning have been getting a lot of interest and shown tremendous performance on visual question answering (VQA) and grounding. But…
Unsupervised Domain Generalization by Learning a Bridge Across Domains
Sivan Harary, Eli Schwartz, Assaf Arbelle +11
The ability to generalize learned representations across significantly different visual domains, such as between real photos, clipart, paintings, and sketches, is a fundamental cap…