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Qian Yang

Doctorat - UdeM
Superviseur⋅e principal⋅e
Aishwarya Agrawal
Sujets de recherche
Apprentissage multimodal
Traitement du langage naturel
Site web
Google Scholar
GitHub

Publications

Learning What Matters: Prioritized Concept Learning via Relative Error-driven Sample Selection
Shivam Chandhok
Qian Yang
Oscar Mañas
Kanishk Jain
Leonid Sigal
Aishwarya Agrawal
Instruction tuning has been central to the success of recent vision-language models (VLMs), but it remains expensive-requiring large-scale d… (voir plus)atasets, high-quality annotations, and large compute budgets. We propose PRioritized cOncept learninG via Relative Error-driven Sample Selection (PROGRESS), a data- and compute-efficient framework that enables VLMs to dynamically select what to learn next based on their evolving needs during training. At each stage, the model tracks its learning progress across skills and selects the most informative samples-those it has not already mastered and that are not too difficult to learn at the current stage of training. This strategy effectively controls skill acquisition and the order in which skills are learned. Specifically, we sample from skills showing the highest learning progress, prioritizing those with the most rapid improvement. Unlike prior methods, PROGRESS requires no upfront answer annotations, queries answers only on a need basis, avoids reliance on additional supervision from auxiliary VLMs, and does not require compute-heavy gradient computations for data selection. Experiments across multiple instruction-tuning datasets of varying scales demonstrate that PROGRESS consistently outperforms state-of-the-art baselines with much less data and supervision. Additionally, we show strong cross-architecture generalization and transferability to larger models, validating PROGRESS as a scalable solution for efficient learning.
2025-06-11
ICML.cc/2025/Workshop/ES-FoMo-III (publié)
doi.org
openreview.net
Assessing and Learning Alignment of Unimodal Vision and Language Models
Le Zhang
Qian Yang
Aishwarya Agrawal
How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment meth… (voir plus)ods do not directly translate to how these models are used in practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to assess vision-language alignment. We identify that the degree of alignment of the SSL vision models depends on their SSL training objective, and we find that the clustering quality of SSL representations has a stronger impact on alignment performance than their linear separability. Next, we introduce Swift Alignment of Image and Language (SAIL), a efficient transfer learning framework that aligns pretrained unimodal vision and language models for downstream vision-language tasks. Since SAIL leverages the strengths of pretrained unimodal models, it requires significantly fewer (6%) paired image-text data for the multimodal alignment compared to models like CLIP which are trained from scratch. SAIL training only requires a single A100 GPU, 5 hours of training and can accommodate a batch size up to 32,768. SAIL achieves 73.4% zero-shot accuracy on ImageNet (vs. CLIP's 72.7%) and excels in zero-shot retrieval, complex reasoning, and semantic segmentation. Additionally, SAIL improves the language-compatibility of vision encoders that in turn enhance the performance of multimodal large language models. The entire codebase and model weights are open-source: https://lezhang7.github.io/sail.github.io/
2025-06-10
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)
doi.org
arxiv.org
Assessing and Learning Alignment of Unimodal Vision and Language Models
Le Zhang
Qian Yang
Aishwarya Agrawal
How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment meth… (voir plus)ods do not directly translate to how these models are used in practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to assess vision-language alignment. We identify that the degree of alignment of the SSL vision models depends on their SSL training objective, and we find that the clustering quality of SSL representations has a stronger impact on alignment performance than their linear separability. Next, we introduce Swift Alignment of Image and Language (SAIL), a efficient transfer learning framework that aligns pretrained unimodal vision and language models for downstream vision-language tasks. Since SAIL leverages the strengths of pretrained unimodal models, it requires significantly fewer (6%) paired image-text data for the multimodal alignment compared to models like CLIP which are trained from scratch. SAIL training only requires a single A100 GPU, 5 hours of training and can accommodate a batch size up to 32,768. SAIL achieves 73.4% zero-shot accuracy on ImageNet (vs. CLIP's 72.7%) and excels in zero-shot retrieval, complex reasoning, and semantic segmentation. Additionally, SAIL improves the language-compatibility of vision encoders that in turn enhance the performance of multimodal large language models. The entire codebase and model weights are open-source: https://lezhang7.github.io/sail.github.io/
2024-12-05
ArXiv (prépublication)
arxiv.org
arxiv.org
Assessing and Learning Alignment of Unimodal Vision and Language Models
Le Zhang
Qian Yang
Aishwarya Agrawal
How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment meth… (voir plus)ods do not directly translate to how these models are used in practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to assess vision-language alignment. We identify that the degree of alignment of the SSL vision models depends on their SSL training objective, and we find that the clustering quality of SSL representations has a stronger impact on alignment performance than their linear separability. Next, we introduce Swift Alignment of Image and Language (SAIL), a efficient transfer learning framework that aligns pretrained unimodal vision and language models for downstream vision-language tasks. Since SAIL leverages the strengths of pretrained unimodal models, it requires significantly fewer (6%) paired image-text data for the multimodal alignment compared to models like CLIP which are trained from scratch. SAIL training only requires a single A100 GPU, 5 hours of training and can accommodate a batch size up to 32,768. SAIL achieves 73.4% zero-shot accuracy on ImageNet (vs. CLIP's 72.7%) and excels in zero-shot retrieval, complex reasoning, and semantic segmentation. Additionally, SAIL improves the language-compatibility of vision encoders that in turn enhance the performance of multimodal large language models. The entire codebase and model weights are open-source: https://lezhang7.github.io/sail.github.io/
2024-12-05
ArXiv (prépublication)
doi.org
arxiv.org
Enhancing Multi-Agent Multi-Modal Collaboration with Fine-Grained Reward Modeling
Qian Yang
Weixiang Yan
Aishwarya Agrawal
Multi-Modal Large Language Models (MLLMs) have significantly advanced multi-modal reasoning but still struggle with compositional reasoning … (voir plus)tasks. Multi-agent collaboration provides a promising solution by leveraging the distinct capabilities of different agents. Specifically, a decomposer agent to handle task breakdown and an answerer agent to generate responses. While there have been efforts to adaptively decompose tasks based on the answerer agent's capabilities, such as using in-context learning, these methods often prove insufficient for fully effective decomposition. We address this issue by enhancing collaboration through fine-grained reward modeling, where each generated sub-question is assigned a specialized reward without requiring extra annotation or tuning of a reward model. Our proposed method dynamically optimizes the decomposition process, enabling better alignment between agents. Experimental results on four vision-language tasks demonstrate consistent improvements, with a 5.5\% absolute increase in mean performance over traditional approaches. These findings highlight the efficacy of fine-grained reward modeling for enhancing multi-agent, multi-modal collaboration.
2024-10-10
NeurIPS.cc/2024/Workshop/AFM (poster)
openreview.net
openreview.net
Visual Language Alignment Tuning
Le Zhang
Qian Yang
Aishwarya Agrawal
2024-10-10
NeurIPS.cc/2024/Workshop/AFM (poster)
openreview.net
openreview.net
Decompose and Compare Consistency: Measuring VLMs' Answer Reliability via Task-Decomposition Consistency Comparison
Qian Yang
Weixiang Yan
Aishwarya Agrawal
Despite tremendous advancements, current state-of-the-art Vision-Language Models (VLMs) are still far from perfect. They tend to hallucinate… (voir plus) and may generate biased responses. In such circumstances, having a way to assess the reliability of a given response generated by a VLM is quite useful. Existing methods, such as estimating uncertainty using answer likelihoods or prompt-based confidence generation, often suffer from overconfidence. Other methods use self-consistency comparison but are affected by confirmation biases. To alleviate these, we propose \textbf{De}compose and \textbf{C}ompare \textbf{C}onsistency (\texttt{DeCC}) for reliability measurement. By comparing the consistency between the direct answer generated using the VLM's internal reasoning process, and the indirect answers obtained by decomposing the question into sub-questions and reasoning over the sub-answers produced by the VLM, \texttt{DeCC} measures the reliability of VLM's direct answer. Experiments across six vision-language tasks with three VLMs show \texttt{DeCC}'s reliability estimation achieves better correlation with task accuracy compared to the existing methods.
2024-07-10
ArXiv (prépublication)
doi.org
arxiv.org