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Found 12 papers, 7 papers with code
Intermediate Level Adversarial Attack for Enhanced Transferability
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models.
QPyTorch: A Low-Precision Arithmetic Simulation Framework
Low-precision training reduces computational cost and produces efficient models.
Streaming Self-Training via Domain-Agnostic Unlabeled Images
We present streaming self-training (SST) that aims to democratize the process of learning visual recognition models such that a non-expert user can define a new task depending on their needs via a few labeled examples and minimal domain knowledge.
The CLEAR Benchmark: Continual LEArning on Real-World Imagery
The major strength of CLEAR over prior CL benchmarks is the smooth temporal evolution of visual concepts with real-world imagery, including both high-quality labeled data along with abundant unlabeled samples per time period for continual semi-supervised learning.
Continual Learning with Evolving Class Ontologies
LECO requires learning classifiers in distinct time periods (TPs); each TP introduces a new ontology of "fine" labels that refines old ontologies of "coarse" labels (e. g., dog breeds that refine the previous ${\tt dog}$).
Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models
By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation.
Revisiting the Role of Language Priors in Vision-Language Models
Our first observation is that they can be repurposed for discriminative tasks (such as image-text retrieval) by simply computing the match score of generating a particular text string given an image.
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Visual Reasoning
on Winoground
Language Models as Black-Box Optimizers for Vision-Language Models
We highlight the advantage of conversational feedback that incorporates both positive and negative prompts, suggesting that LLMs can utilize the implicit gradient direction in textual feedback for a more efficient search.
Prompting Scientific Names for Zero-Shot Species Recognition
We find that common names are more likely to be included in CLIP's training set, and prompting them achieves 2$\sim$5 times higher accuracy on benchmarking datasets of fine-grained species recognition.
The Neglected Tails of Vision-Language Models
We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts.
Evaluating Text-to-Visual Generation with Image-to-Text Generation
For instance, the widely-used CLIPScore measures the alignment between a (generated) image and text prompt, but it fails to produce reliable scores for complex prompts involving compositions of objects, attributes, and relations.
