SA-CLIP: Language Guided Image Spatial and Action Feature Learning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Contrastive language-image pretraining models struggle with real-world downstream tasks such as road traffic anomaly detection due to inability to effectively capture spatial and action relationships between objects within images. |
| Approach: | They compile and curate a dataset and train a Spatial and Action relationship aware CLIP model. |
| Outcome: | The proposed model performs well on the traffic anomaly detection task . |
Similar Papers
RWKV-CLIP: A Robust Vision-Language Representation Learner (2024.emnlp-main)
Copied to clipboard
| Challenge: | Using large image-text datasets, large-scale image-data sets have been used for visionlanguage pre-training. |
| Approach: | They propose a framework that leverages Large Language Models to combine and refine information from web-based image-text pairs, synthetic captions, and detection tags. |
| Outcome: | The proposed framework can combine and refine information from web-based image-text pairs, synthetic captions, and detection tags. |
Delving into the Openness of CLIP (2023.findings-acl)
Copied to clipboard
| Challenge: | Contrastive Language-Image Pre-training (CLIP) allows for open-vocabulary visual recognition, where the model can recognize images from an open class set in a zero-shot manner. |
| Approach: | They propose to use image classification as an image-to-text matching task instead of discrete category IDs to achieve open-vocabulary visual recognition. |
| Outcome: | The proposed model can recognize images from an open vocabulary in a zero-shot manner, but its performance deteriorates as the vocabulary expands. |
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to build a strong multilingual multimodal representation model are lacking in good-quality text-image pairs. |
| Approach: | They propose a method to build a strong multilingual multimodal representation model using English text-image pairs instead of a model from scratch. |
| Outcome: | The proposed model outperforms the original CLIP model on multilingual multimodal benchmarks. |
Cross-lingual and Multilingual CLIP (2022.lrec-1)
Copied to clipboard
| Challenge: | OpenAI released CLIP, a model that relates the textual and visual domains with unprecedented accuracy. |
| Approach: | They propose to use cross-lingual teacher learning to re-train an English textual encoder using a large dataset of images and captions. |
| Outcome: | The proposed method outperforms baselines on multilingual image-text retrieval while retaining low cost. |
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding (2021.emnlp-main)
Copied to clipboard
Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, Christoph Feichtenhofer
| Challenge: | Recent work adopts a "pre-training + fine-tuning" approach for zero-shot transfer to end tasks without fine- tuning. |
| Approach: | They propose a contrastive approach to pre-train a transformer model for zero-shot video and text understanding without using any labels on downstream tasks. |
| Outcome: | The proposed model outperforms supervised approaches on downstream tasks and outperformed previous approaches. |
Paparazzi: A Deep Dive into the Capabilities of Language and Vision Models for Grounding Viewpoint Descriptions (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing language and vision models can be used for language understanding in 3D environments . however, existing models lack specific properties and biases that limit their performance . |
| Approach: | They propose a framework that uses a camera to generate images from different viewpoints and evaluate them in terms of their similarity to natural language descriptions. |
| Outcome: | The proposed model performs poorly on most canonical views and fine-tunes using hard negative sampling and random contrasting yields good results even under conditions with little available training data. |
CLIP Models are Few-Shot Learners: Empirical Studies on VQA and Visual Entailment (2022.acl-long)
Copied to clipboard
| Challenge: | Previously, CLIP was only regarded as a powerful visual encoder. |
| Approach: | They propose a parameter-efficient fine-tuning strategy to boost CLIP's few-shot performance on a visual entailment task without introducing any additional pre-training procedure. |
| Outcome: | The proposed strategy achieves competitive zero/few-shot results on visual question answering and visual entailment tasks without introducing any additional pre-training procedure. |
Developing Japanese CLIP Models Leveraging an Open-weight LLM for Large-scale Dataset Translation (2025.naacl-srw)
Copied to clipboard
| Challenge: | lack of large-scale open Japanese image-text pairs poses a significant barrier to the development of vision-language models. |
| Approach: | They construct large-scale Japanese image-text pairs using machine translation and pre-trained CLIP models on a Japanese dataset. |
| Outcome: | The results show that pre-trained models achieve competitive average scores on Japanese culture tasks compared to models of similar size. |
Language over Labels: Contrastive Language Supervision Exceeds Purely Label-Supervised Classification Performance on Chest X-Rays (2022.aacl-srw)
Copied to clipboard
| Challenge: | Pretrained CLIP models lack domain-specific knowledge of text and images. |
| Approach: | They adapt CLIP-based models to the chest radiography domain using contrastive language supervision and a detailed ablation study of the batch and dataset size. |
| Outcome: | The proposed model outperforms supervised learning on labels on the MIMIC-CXR dataset while generalizing to the CheXpert and RSNA Pneumonia datasets. |
CLIPText: A New Paradigm for Zero-shot Text Classification (2023.findings-acl)
Copied to clipboard
| Challenge: | Experimental results show that CLIP can be applied to zero-shot text classification tasks. |
| Approach: | They propose a CLIP model for zero-shot text classification that integrates prompt into CLIPText to better derive knowledge from CLIP. |
| Outcome: | The proposed model can be applied to a text-image matching problem and show that it can be used for language tasks. |