13 citations · 33 across the 9 of their papers we have counts for
12 papers
Do LLMs Really Need 10+ Thoughts for "Find the Time 1000 Days Later"? Towards Structural Understanding of LLM Overthinking
Xinliang Frederick Zhang, Anhad Mohananey, Alexandra Chronopoulou +5
Models employing long chain-of-thought (CoT) reasoning have shown superior performance on complex reasoning tasks. Yet, this capability introduces a critical and often overlooked i…
Dynamic Classifier-Free Diffusion Guidance via Online Feedback
Pinelopi Papalampidi, Olivia Wiles, Ira Ktena +5
Classifier-free guidance (CFG) is a cornerstone of text-to-image diffusion models, yet its effectiveness is limited by the use of static guidance scales. This "one-size-fits-all" a…
PaliGemma: A versatile 3B VLM for transfer
Lucas Beyer, Andreas Steiner, André Susano Pinto +32
PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly know…
Memory Consolidation Enables Long-Context Video Understanding
Ivana Balažević, Yuge Shi, Pinelopi Papalampidi +3
Most transformer-based video encoders are limited to short temporal contexts due to their quadratic complexity. While various attempts have been made to extend this context, this h…
A Simple Recipe for Contrastively Pre-training Video-First Encoders Beyond 16 Frames
Pinelopi Papalampidi, Skanda Koppula, Shreya Pathak +7
Understanding long, real-world videos requires modeling of long-range visual dependencies. To this end, we explore video-first architectures, building on the common paradigm of tra…
Hierarchical3D Adapters for Long Video-to-text Summarization
Pinelopi Papalampidi, Mirella Lapata
In this paper, we focus on video-to-text summarization and investigate how to best utilize multimodal information for summarizing long inputs (e.g., an hour-long TV show) into long…
Towards Coherent and Consistent Use of Entities in Narrative Generation
Pinelopi Papalampidi, Kris Cao, Tomas Kocisky
Large pre-trained language models (LMs) have demonstrated impressive capabilities in generating long, fluent text; however, there is little to no analysis on their ability to maint…
Finding the Right Moment: Human-Assisted Trailer Creation via Task Composition
Pinelopi Papalampidi, Frank Keller, Mirella Lapata
Movie trailers perform multiple functions: they introduce viewers to the story, convey the mood and artistic style of the film, and encourage audiences to see the movie. These dive…
Movie Summarization via Sparse Graph Construction
Pinelopi Papalampidi, Frank Keller, Mirella Lapata
We summarize full-length movies by creating shorter videos containing their most informative scenes. We explore the hypothesis that a summary can be created by assembling scenes wh…
Screenplay Summarization Using Latent Narrative Structure
Pinelopi Papalampidi, Frank Keller, Lea Frermann +1
Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront. As a result, such models are biase…
Movie Plot Analysis via Turning Point Identification
Pinelopi Papalampidi, Frank Keller, Mirella Lapata
According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure,…
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs
Christos Baziotis, Nikos Athanasiou, Pinelopi Papalampidi +4
In this paper we present two deep-learning systems that competed at SemEval-2018 Task 3 "Irony detection in English tweets". We design and ensemble two independent models, based on…