7 citations · 16 across the 21 of their papers we have counts for
25 papers
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…
TeMo: Temperature Modulation for Multimodal Contrastive Learning
Dhimitrios Duka, Bernt Schiele, Hilde Kuehne +1
Contrastive learning approaches achieve strong performance by training models to bring similar samples closer while pushing dissimilar samples apart. A crucial component of contras…
Do Instance Priors Help Weakly Supervised Semantic Segmentation?
Anurag Das, Anna Kukleva, Xinting Hu +2
Semantic segmentation requires dense pixel-level annotations, which are costly and time-consuming to acquire. To address this, we present SeSAM, a framework that uses a foundationa…
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,…
MM-TS: Multi-Modal Temperature and Margin Schedules for Contrastive Learning with Long-Tail Data
Siarhei Sheludzko, Dhimitrios Duka, Bernt Schiele +2
Contrastive learning has become a fundamental approach in both uni-modal and multi-modal frameworks. This learning paradigm pulls positive pairs of samples closer while pushing neg…
When LLaVA Meets Objects: Token Composition for Vision-Language-Models
Soumya Jahagirdar, Walid Bousselham, Anna Kukleva +1
Current autoregressive Vision Language Models (VLMs) usually rely on a large number of visual tokens to represent images, resulting in a need for more compute especially at inferen…