Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding

• URL: http://arxiv.org/abs/2312.00081v2
• Date: Sat, 30 Mar 2024 12:45:08 GMT
• Title: Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
• Authors: Wujian Peng, Sicheng Xie, Zuyao You, Shiyi Lan, Zuxuan Wu,
• Abstract summary: Vision language models (VLM) have demonstrated remarkable performance across various downstream tasks. However, understanding fine-grained visual-linguistic concepts, such as attributes and inter-object relationships, remains a significant challenge. We introduce a progressive pipeline to synthesize images that vary in a specific attribute while ensuring consistency in all other aspects.
• Score: 33.33424214458285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision language models (VLM) have demonstrated remarkable performance across various downstream tasks. However, understanding fine-grained visual-linguistic concepts, such as attributes and inter-object relationships, remains a significant challenge. While several benchmarks aim to evaluate VLMs in finer granularity, their primary focus remains on the linguistic aspect, neglecting the visual dimension. Here, we highlight the importance of evaluating VLMs from both a textual and visual perspective. We introduce a progressive pipeline to synthesize images that vary in a specific attribute while ensuring consistency in all other aspects. Utilizing this data engine, we carefully design a benchmark, SPEC, to diagnose the comprehension of object size, position, existence, and count. Subsequently, we conduct a thorough evaluation of four leading VLMs on SPEC. Surprisingly, their performance is close to random guess, revealing significant limitations. With this in mind, we propose a simple yet effective approach to optimize VLMs in fine-grained understanding, achieving significant improvements on SPEC without compromising the zero-shot performance. Results on two additional fine-grained benchmarks also show consistent improvements, further validating the transferability of our approach. Code and data are available at https://github.com/wjpoom/SPEC.

Related papers

• QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video Comprehension [86.0749609778104]
  We propose QuoTA, an ante-hoc training-free modular that extends existing large video-language models. QuoTA strategically allocates frame-level importance scores based on query relevance. We decouple the query through Chain-of-Thoughts reasoning to facilitate more precise LVLM-based frame importance scoring.
  arXiv  Detail & Related papers  (2025-03-11T17:59:57Z)
• Symmetrical Visual Contrastive Optimization: Aligning Vision-Language Models with Minimal Contrastive Images [7.823336661261962]
  Large Vision-Language Models (VLMs) tend to neglect image content and over-rely on language-model priors. We propose S-VCO (Symmetrical Visual Contrastive Optimization), a novel finetuning objective that steers the model toward capturing important visual details.
  arXiv  Detail & Related papers  (2025-02-19T18:05:42Z)
• Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks [41.488394198111976]
  Vision language models (VLMs) like CLIP show stellar zero-shot capability on classification benchmarks. selecting the VLM with the highest performance on the unlabeled downstream task is non-trivial. This paper introduces the problem of textbfunsupervised vision-language model selection, where only unsupervised downstream datasets are available.
  arXiv  Detail & Related papers  (2024-12-30T03:26:53Z)
• Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models [50.98559225639266]
  We investigate the contributions of visual features from different encoder layers using 18 benchmarks spanning 6 task categories. Our findings reveal that multilayer features provide complementary strengths with varying task dependencies, and uniform fusion leads to suboptimal performance. We propose the instruction-guided vision aggregator, a module that dynamically integrates multi-layer visual features based on textual instructions.
  arXiv  Detail & Related papers  (2024-12-26T05:41:31Z)
• Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension [95.63899307791665]
  Vision Value Model (VisVM) can guide VLM inference-time search to generate responses with better visual comprehension. In this paper, we present VisVM that can guide VLM inference-time search to generate responses with better visual comprehension.
  arXiv  Detail & Related papers  (2024-12-04T20:35:07Z)
• A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMs [65.00970402080351]
  A promising approach to accelerating large vision-language models (VLMs) is using partial information, such as attention maps from specific layers, to assess token importance and prune less essential tokens. Our study reveals three key insights: (i) Partial attention information is insufficient for accurately identifying critical visual tokens, resulting in suboptimal performance, especially at low token retention ratios; (ii) Global attention information, such as the attention map aggregated across all layers, more effectively preserves essential tokens and maintains comparable performance under aggressive pruning; and (iii) The global attention map aggregated from a small VLM closely resembles that of a large VLM,
  arXiv  Detail & Related papers  (2024-12-04T13:56:44Z)
• VHELM: A Holistic Evaluation of Vision Language Models [75.88987277686914]
  We present the Holistic Evaluation of Vision Language Models (VHELM) VHELM aggregates various datasets to cover one or more of the 9 aspects: visual perception, knowledge, reasoning, bias, fairness, multilinguality, robustness, toxicity, and safety. Our framework is designed to be lightweight and automatic so that evaluation runs are cheap and fast.
  arXiv  Detail & Related papers  (2024-10-09T17:46:34Z)
• Intriguing Properties of Large Language and Vision Models [18.449076451976236]
  Large language and vision models (LLVMs) have received significant attention and development efforts due to their remarkable generalization performance. Despite their achievements in advanced reasoning tasks, their performance on fundamental perception-related tasks remains surprisingly low. We investigate this question by evaluating the most common LLVM's families (i.e., LLaVA) across 10 evaluation benchmarks.
  arXiv  Detail & Related papers  (2024-10-07T05:07:01Z)
• Response Wide Shut: Surprising Observations in Basic Vision Language Model Capabilities [30.176918208200604]
  Vision-Language Models (VLMs) have emerged as general purpose tools for addressing a variety of complex computer vision problems. These models have been shown to be highly capable, but also lacking some basic visual understanding skills. This paper sets out to understand the limitations of SoTA VLMs on fundamental visual tasks.
  arXiv  Detail & Related papers  (2024-08-13T08:26:32Z)
• How Well Can Vision Language Models See Image Details? [53.036922527685064]
  We introduce a pixel value prediction task to explore "How Well Can Vision Language Models See Image Details?" Our research reveals that incorporating pixel value prediction as one of the VLM pre-training tasks and vision encoder adaptation markedly boosts VLM performance on downstream image-language understanding tasks.
  arXiv  Detail & Related papers  (2024-08-07T17:59:40Z)
• BEAF: Observing BEfore-AFter Changes to Evaluate Hallucination in Vision-language Models [20.697019266074747]
  Vision language models (VLMs) perceive the world through a combination of a visual encoder and a large language model (LLM) Recent studies show that VLMs are vulnerable to hallucination. We introduce new metrics: True Understanding (TU), IGnorance (IG), StuBbornness (SB), and InDecision (ID)
  arXiv  Detail & Related papers  (2024-07-18T12:11:12Z)
• Towards Semantic Equivalence of Tokenization in Multimodal LLM [149.11720372278273]
  Vision tokenization is essential for semantic alignment between vision and language. This paper proposes a novel dynamic Semantic-Equivalent Vision Tokenizer (SeTok) SeTok groups visual features into semantic units via a dynamic clustering algorithm. The resulting vision tokens effectively preserve semantic integrity and capture both low-frequency and high-frequency visual features.
  arXiv  Detail & Related papers  (2024-06-07T17:55:43Z)
• Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement [102.22911097049953]
  SIMA is a framework that enhances visual and language modality alignment through self-improvement. It employs an in-context self-critic mechanism to select response pairs for preference tuning. We demonstrate that SIMA achieves superior modality alignment, outperforming previous approaches.
  arXiv  Detail & Related papers  (2024-05-24T23:09:27Z)
• CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models [58.95889895912716]
  We introduce a new benchmark, named as CODIS, designed to assess the ability of models to use context provided in free-form text to enhance visual comprehension. Our findings indicate that MLLMs consistently fall short of human performance on this benchmark. This underscores the pressing need to enhance the ability of MLLMs to comprehend visuals in a context-dependent manner.
  arXiv  Detail & Related papers  (2024-02-21T08:21:12Z)
• What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning [115.19451843294154]
  Visual instruction tuning is an essential approach to improving the zero-shot generalization capability of Multi-modal Large Language Models (MLLMs) We propose a systematic approach to automatically creating high-quality complex visual reasoning instructions. Our dataset consistently enhances the performance of all the compared MLLMs, e.g., improving the performance of MiniGPT-4 and BLIP-2 on MME-Cognition by 32.6% and 28.8%, respectively.
  arXiv  Detail & Related papers  (2023-11-02T15:36:12Z)
• Visual Data-Type Understanding does not emerge from Scaling Vision-Language Models [31.69213233651326]
  We introduce the novel task of Visual Data-Type Identification. An extensive zero-shot evaluation of 39 vision-language models (VLMs) shows a nuanced performance landscape.
  arXiv  Detail & Related papers  (2023-10-12T17:59:30Z)
• What Makes for Good Visual Tokenizers for Large Language Models? [26.488269091290597]
  We investigate proper pre-training methods to build good visual tokenizers, making Large Language Models (LLMs) powerful Multimodal Large Language Models (MLLMs) We discuss different visual tokenizers pre-trained with dominant methods (i.e., DeiT, CLIP, MAE, DINO) We obtain a new MLLM equipped with a tailored Good Visual Tokenizer (GVT), which exhibits strong visual comprehension capability at multiple scales.
  arXiv  Detail & Related papers  (2023-05-20T16:11:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.