# Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition

arXiv: https://arxiv.org/abs/2012.13073 · HTML: https://mlsanity.com/paper/2012.13073/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 14 of 14)

- [DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection](https://mlsanity.com/paper/2303.09674/) (2023)
- [Open-Set Likelihood Maximization for Few-Shot Learning](https://mlsanity.com/paper/2301.08390/) (2023)
- [Glocal Energy-based Learning for Few-Shot Open-Set Recognition](https://mlsanity.com/paper/2304.11855/) (2023)
- [Towards Generalized Few-Shot Open-Set Object Detection](https://mlsanity.com/paper/2210.15996/) (2022)
- [Enhancing Vision-Language Few-Shot Adaptation with Negative Learning](https://mlsanity.com/paper/2403.12964/) (2024)
- [A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection](https://mlsanity.com/paper/2209.12935/) (2022)
- [Towards Few-Shot Learning in the Open World: A Review and Beyond](https://mlsanity.com/paper/2408.09722/) (2024)
- [HeGraphAdapter: Tuning Multi-Modal Vision-Language Models with Heterogeneous Graph Adapter](https://mlsanity.com/paper/2410.07854/) (2024)
- [FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?](https://mlsanity.com/paper/2307.04114/) (2023)
- [Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes](https://mlsanity.com/paper/2607.01297/) (2026)
- [LLM meets Vision-Language Models for Zero-Shot One-Class Classification](https://mlsanity.com/paper/2404.00675/) (2024)
- [Unlocking Transfer Learning for Open-World Few-Shot Recognition](https://mlsanity.com/paper/2411.09986/) (2024)
- [A Baseline Study and Benchmark for Few-Shot Open-Set Action Recognition with Feature Residual Discrimination](https://mlsanity.com/paper/2603.04125/) (2026)
- [Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement](https://mlsanity.com/paper/2607.26607/) (2026)

## Abstract (arXiv)

We study the problem of few-shot open-set recognition (FSOR), which learns a recognition system capable of both fast adaptation to new classes with limited labeled examples and rejection of unknown negative samples. Traditional large-scale open-set methods have been shown ineffective for FSOR problem due to data limitation. Current FSOR methods typically calibrate few-shot closed-set classifiers to be sensitive to negative samples so that they can be rejected via thresholding. However, threshold tuning is a challenging process as different FSOR tasks may require different rejection powers. In this paper, we instead propose task-adaptive negative class envision for FSOR to integrate threshold tuning into the learning process. Specifically, we augment the few-shot closed-set classifier with additional negative prototypes generated from few-shot examples. By incorporating few-shot class correlations in the negative generation process, we are able to learn dynamic rejection boundaries for FSOR tasks. Besides, we extend our method to generalized few-shot open-set recognition (GFSOR), which requires classification on both many-shot and few-shot classes as well as rejection of negative samples. Extensive experiments on public benchmarks validate our methods on both problems.