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Alessandro Conti

7 accepted papers

2026

Specificity-aware reinforcement learning for fine-grained open-world classification

CVPR 2026

Classifying fine-grained visual concepts under open-world settings, i.e., without a predefined label set, demands models to be both accurate and specific. Recent reasoning Large Multimodal Models (LMMs) exhibit strong visual understanding capability but tend to produce overly generic predictions whe

Cited by 0SourcecodeScholar
2025

Compositional Caching for Training-free Open-vocabulary Attribute Detection

CVPR 2025highlight

Attribute detection is crucial for many computer vision tasks, as it enables systems to describe properties such as color, texture, and material. Current approaches often rely on labor-intensive annotation processes which are inherently limited: objects can be described at an arbitrary level of deta…

Cited by 0SourcePDFScholar
2025

ConViS-Bench: Estimating Video Similarity Through Semantic Concepts

NeurIPS 2025poster

What does it mean for two videos to be similar? Videos may appear similar when judged by the actions they depict, yet entirely different if evaluated based on the locations where they were filmed. While humans naturally compare videos by taking different aspects into account, this ability has not be…

Cited by 0SourceScholar
2025

On Large Multimodal Models as Open-World Image Classifiers

ICCV 2025poster

Traditional image classification requires a predefined list of semantic categories. In contrast, Large Multimodal Models (LMMs) can sidestep this requirement by classifying images directly using natural language (e.g., answering the prompt "What is the main object in the image?"). Despite this remar…

2024

Test-Time Zero-Shot Temporal Action Localization

CVPR 2024poster

Zero-Shot Temporal Action Localization (ZS-TAL) seeks to identify and locate actions in untrimmed videos unseen during training. Existing ZS-TAL methods involve fine-tuning a model on a large amount of annotated training data. While effective training-based ZS-TAL approaches assume the availability…

2023

The Unreasonable Effectiveness of Large Language-Vision Models for Source-Free Video Domain Adaptation

ICCV 2023poster

Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in adapting an action recognition model, trained on a labelled source dataset, to an unlabelled target dataset, without accessing the actual source data. The previous approaches have attempted to address SFVUDA by leveraging sel…

Cited by 11PDFcodeScholar
2023

Vocabulary-free Image Classification

NeurIPS 2023poster

Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categories, a.k.a. the vocabulary, is assumed at test time for composing the textual prompts. However, such assumption can be…