2.2k citations · 5.2k across the 82 of their papers we have counts for
95 papers
PDiscoNet: Semantically consistent part discovery for fine-grained recognition
Robert van der Klis, Stephan Alaniz, Massimiliano Mancini +4
Fine-grained classification often requires recognizing specific object parts, such as beak shape and wing patterns for birds. Encouraging a fine-grained classification model to fir…
Iterative Superquadric Recomposition of 3D Objects from Multiple Views
Stephan Alaniz, Massimiliano Mancini, Zeynep Akata
Humans are good at recomposing novel objects, i.e. they can identify commonalities between unknown objects from general structure to finer detail, an ability difficult to replicate…
DeViL: Decoding Vision features into Language
Meghal Dani, Isabel Rio-Torto, Stephan Alaniz +1
Post-hoc explanation methods have often been criticised for abstracting away the decision-making process of deep neural networks. In this work, we would like to provide natural lan…
Image-free Classifier Injection for Zero-Shot Classification
Anders Christensen, Massimiliano Mancini, A. Sophia Koepke +2
Zero-shot learning models achieve remarkable results on image classification for samples from classes that were not seen during training. However, such models must be trained from…
Addressing caveats of neural persistence with deep graph persistence
Leander Girrbach, Anders Christensen, Ole Winther +2
Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning. In this work, how…
ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models
Uddeshya Upadhyay, Shyamgopal Karthik, Massimiliano Mancini +1
Large-scale vision-language models (VLMs) like CLIP successfully find correspondences between images and text. Through the standard deterministic mapping process, an image or a tex…
Waffling around for Performance: Visual Classification with Random Words and Broad Concepts
Karsten Roth, Jae Myung Kim, A. Sophia Koepke +3
The visual classification performance of vision-language models such as CLIP has been shown to benefit from additional semantic knowledge from large language models (LLMs) such as…
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution
Vikrant Rangnekar, Uddeshya Upadhyay, Zeynep Akata +1
Dense regression is a widely used approach in computer vision for tasks such as image super-resolution, enhancement, depth estimation, etc. However, the high cost of annotation and…
If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection
Shyamgopal Karthik, Karsten Roth, Massimiliano Mancini +1
Despite their impressive capabilities, diffusion-based text-to-image (T2I) models can lack faithfulness to the text prompt, where generated images may not contain all the mentioned…
Meta-in-context learning in large language models
Julian Coda-Forno, Marcel Binz, Zeynep Akata +3
Large language models have shown tremendous performance in a variety of tasks. In-context learning -- the ability to improve at a task after being provided with a number of demonst…
In-Context Impersonation Reveals Large Language Models' Strengths and Biases
Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto +2
In everyday conversations, humans can take on different roles and adapt their vocabulary to their chosen roles. We explore whether LLMs can take on, that is impersonate, different…
Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval
Jae Myung Kim, A. Sophia Koepke, Cordelia Schmid +1
Cross-modal retrieval methods are the preferred tool to search databases for the text that best matches a query image and vice versa. However, image-text retrieval models commonly…
Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification
Youngwook Kim, Jae Myung Kim, Jieun Jeong +3
Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer visi…
Likelihood Annealing: Fast Calibrated Uncertainty for Regression
Uddeshya Upadhyay, Jae Myung Kim, Cordelia Schmidt +2
Recent advances in deep learning have shown that uncertainty estimation is becoming increasingly important in applications such as medical imaging, natural language processing, and…
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation
Anurag Das, Yongqin Xian, Yang He +2
For best performance, today's semantic segmentation methods use large and carefully labeled datasets, requiring expensive annotation budgets. In this work, we show that coarse anno…
Distilling Knowledge from Self-Supervised Teacher by Embedding Graph Alignment
Yuchen Ma, Yanbei Chen, Zeynep Akata
Recent advances have indicated the strengths of self-supervised pre-training for improving representation learning on downstream tasks. Existing works often utilize self-supervised…
Momentum-based Weight Interpolation of Strong Zero-Shot Models for Continual Learning
Zafir Stojanovski, Karsten Roth, Zeynep Akata
Large pre-trained, zero-shot capable models have shown considerable success both for standard transfer and adaptation tasks, with particular robustness towards distribution shifts.…
Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval
Abhra Chaudhuri, Massimiliano Mancini, Yanbei Chen +2
Representation learning for sketch-based image retrieval has mostly been tackled by learning embeddings that discard modality-specific information. As instances from different moda…
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
Abhra Chaudhuri, Massimiliano Mancini, Zeynep Akata +1
Fine-grained categories that largely share the same set of parts cannot be discriminated based on part information alone, as they mostly differ in the way the local parts relate to…
Disentanglement of Correlated Factors via Hausdorff Factorized Support
Karsten Roth, Mark Ibrahim, Zeynep Akata +2
A grand goal in deep learning research is to learn representations capable of generalizing across distribution shifts. Disentanglement is one promising direction aimed at aligning…
Semantic Image Synthesis with Semantically Coupled VQ-Model
Stephan Alaniz, Thomas Hummel, Zeynep Akata
Semantic image synthesis enables control over unconditional image generation by allowing guidance on what is being generated. We conditionally synthesize the latent space from a ve…
Semi-Supervised and Unsupervised Deep Visual Learning: A Survey
Yanbei Chen, Massimiliano Mancini, Xiatian Zhu +1
State-of-the-art deep learning models are often trained with a large amount of costly labeled training data. However, requiring exhaustive manual annotations may degrade the model'…
Abstracting Sketches through Simple Primitives
Stephan Alaniz, Massimiliano Mancini, Anjan Dutta +2
Humans show high-level of abstraction capabilities in games that require quickly communicating object information. They decompose the message content into multiple parts and commun…
Temporal and cross-modal attention for audio-visual zero-shot learning
Otniel-Bogdan Mercea, Thomas Hummel, A. Sophia Koepke +1
Audio-visual generalised zero-shot learning for video classification requires understanding the relations between the audio and visual information in order to be able to recognise…