Zero-Shot Image Classification
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Benchmarks
Most implemented
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
Reproducible scaling laws for contrastive language-image learning
LiT: Zero-Shot Transfer with Locked-image text Tuning
Papers
G2D: Generative-to-Discriminative Collaborative Inference for Zero-Shot Image Classification
Zero-shot classification needs efficient label retrieval and fine-grained visual reasoning, yet discriminative and generative vision-language models fail in complementary ways.When CLIP's top-1 prediction is wrong, the c…
Zero-Shot Image ClassificationVisual ReasoningFrom Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model
Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference. Although significant efforts are devoted to adapting VLMs at test…
Zero-Shot Image ClassificationContrastive LearningClimateVID -- Social Media Videos Analysis and Challenges Involved
The pervasive growth of digital content, specifically short videos on social media platforms, has significantly altered how topics are discussed and understood in public discourse. In this work, we advance automated visu…
Zero-Shot Image ClassificationBeyond Standard Benchmarks: A Systematic Audit of Vision-Language Model's Robustness to Natural Semantic Variation Across Diverse Tasks
Recent advances in vision-language models (VLMs) trained on web-scale image-text pairs have enabled impressive zero-shot transfer across a diverse range of visual tasks. However, comprehensive and independent evaluation …
Zero-Shot Image ClassificationVisual Question AnsweringSemantic SegmentationA Closed-Form Solution for Debiasing Vision-Language Models with Utility Guarantees Across Modalities and Tasks
While Vision-Language Models (VLMs) have achieved remarkable performance across diverse downstream tasks, recent studies have shown that they can inherit social biases from the training data and further propagate them in…
Zero-Shot Image ClassificationText-to-Image GenerationImage RetrievalBeyond Heuristic Prompting: A Concept-Guided Bayesian Framework for Zero-Shot Image Recognition
Vision-Language Models (VLMs), such as CLIP, have significantly advanced zero-shot image recognition. However, their performance remains limited by suboptimal prompt engineering and poor adaptability to target classes. W…
Zero-Shot Image ClassificationPrompt Engineering