10 papers
The Quest for Winning Tickets in Low-Rank Adapters
Hamed Damirchi, Cristian Rodriguez-Opazo, Ehsan Abbasnejad +2
The Lottery Ticket Hypothesis (LTH) suggests that over-parameterized neural networks contain sparse subnetworks ("winning tickets") capable of matching full model performance when…
Frame-wise Conditioning Adaptation for Fine-Tuning Diffusion Models in Text-to-Video Prediction
Zheyuan Liu, Junyan Wang, Zicheng Duan +2
Text-video prediction (TVP) is a downstream video generation task that requires a model to produce subsequent video frames given a series of initial video frames and text describin…
An empirical study of the effect of video encoders on Temporal Video Grounding
Ignacio M. De la Jara, Cristian Rodriguez-Opazo, Edison Marrese-Taylor +1
Temporal video grounding is a fundamental task in computer vision, aiming to localize a natural language query in a long, untrimmed video. It has a key role in the scientific commu…
Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection
I. M. De la Jara, C. Rodriguez-Opazo, D. Teney +2
Out-of-distribution (OOD) detection is essential for reliably deploying machine learning models in the wild. Yet, most methods treat large pre-trained models as monolithic encoders…
RandLoRA: Full-rank parameter-efficient fine-tuning of large models
Paul Albert, Frederic Z. Zhang, Hemanth Saratchandran +3
Low-Rank Adaptation (LoRA) and its variants have shown impressive results in reducing the number of trainable parameters and memory requirements of large transformer networks while…
Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling
Cristian Rodriguez-Opazo, Ehsan Abbasnejad, Damien Teney +3
Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. Various architectures, from vision transformers (ViTs) to convolut…