Papers with image generation tasks
Do NOT Classify and Count: Hybrid Attribute Control Success Evaluation (2026.eacl-long)
Copied to clipboard
| Challenge: | evaluating attribute control success in controllable text generation relies on pretrained classifiers. |
| Approach: | They propose a Bayesian method that combines classifier predictions with a small number of human labels for calibration. |
| Outcome: | The proposed method produces robust estimates across both text and image generation tasks, offering an alternative to current evaluation practices. |
Can CLIP Count Stars? An Empirical Study on Quantity Bias in CLIP (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Despite its versatility, CLIP-based applications often suffer from misunderstandings regarding user intent, leading to discrepancies between the required number of objects and the actual outputs. |
| Approach: | They empirically evaluate CLIP’s understanding of quantity from text, image, and cross-modal perspectives by carefully designing different experimental settings and datasets. |
| Outcome: | The proposed model has shown significant success in various downstream tasks, including editing, generation, and quality evaluation. |
When the LM misunderstood the human chuckled: Analyzing garden path effects in humans and language models (2025.acl-long)
Copied to clipboard
| Challenge: | Modern Large Language Models (LLMs) have shown human-like abilities in many language tasks, sparking interest in comparing LLMs’ and humans’ language processing. |
| Approach: | They propose to answer two questions: 1. What makes garden-path sentences hard for humans? 2. Do the same reasons make garden- path sentences hard? |
| Outcome: | The proposed models show that humans struggle with specific syntactic complexities, with some models showing high correlation with human comprehension. |
Forget the Token and Pixel: Rethinking Gradient Ascent for Concept Unlearning in Multimodal Generative Models (2025.findings-acl)
Copied to clipboard
Jiaqi Li, Chuanyi Zhang, Miaozeng Du, Hui Zhang, Yongrui Chen, Qianshan Wei, Junfeng Fang, Ruipeng Wang, Sheng Bi, Guilin Qi
| Challenge: | Gradient Ascent (GA) has emerged as a promising approach for concept unlearning in Multimodal Generative Models (MGMs). |
| Approach: | They propose a novel approach that selectively applies GA to targeted Conceptual Knowledge while preserving Natural Knowledge through Gradient Descent (GD). |
| Outcome: | The proposed approach removes Conceptual Knowledge and inadvertently diminishes Natural Knowledge, resulting in utility degradation. |
DiffS2UT: A Semantic Preserving Diffusion Model for Textless Direct Speech-to-Speech Translation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for speech generation are not efficient due to low information density of speech data. |
| Approach: | They propose a method to integrate discrete diffusion models into speech generation tasks . they propose to apply diffusion forward process while employing diffusion backward process . |
| Outcome: | The proposed model achieves comparable results to the auto-regressive baselines with significantly fewer decoding steps (50 steps). |