6 papers · 1 filter
Representation Trajectories Matters: Complementary Evidence for OOD Detection and Image Classification
Ignacio M. De la Jara, Cristian Rodriguez-Opazo, Hamed Damirchi +2
Vision models do not form a representation at once; each block revises it. We ask whether the resulting computation path contains evidence that the final representation discards, a…
Vertical Fusion: Condensing Internal Representations for Robust ViT Classification
Francesco Di Salvo, Shyam Nandan Rai, Hamed Damirchi +4
Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is conside…
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…
Unveiling Backbone Effects in CLIP: Exploring Representational Synergies and Variances
Cristian Rodriguez-Opazo, Edison Marrese-Taylor, Ehsan Abbasnejad +4
Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. Various neural architectures, spanning Transformer-based models li…
Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines
Hamed Damirchi, Cristian Rodríguez-Opazo, Ehsan Abbasnejad +4
Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box…
Independent Modular Networks
Hamed Damirchi, Forest Agostinelli, Pooyan Jamshidi
Monolithic neural networks that make use of a single set of weights to learn useful representations for downstream tasks explicitly dismiss the compositional nature of data generat…