Journal: Visual Intelligence

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Springer

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ISSN

2731-9008

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2024 - 20252
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Publications 1 - 2 of 2
  • When SAM2 meets video camouflaged object segmentation: a comprehensive evaluation and adaptation
    Item type: Journal Article
    Zhou, Yuli; Sun, Guolei; Li, Yawei; et al. (2025)
    Visual Intelligence
    This study investigates the application and performance of the Segment Anything Model 2 (SAM2) in the challenging task of video camouflaged object segmentation (VCOS). VCOS involves detecting objects that blend seamlessly in the surroundings for videos due to similar colors and textures and poor light conditions. Compared to the objects in normal scenes, camouflaged objects are much more difficult to detect. SAM2, a video foundation model, has shown potential in various tasks. However, its effectiveness in dynamic camouflaged scenarios remains under-explored. This study presents a comprehensive study on SAM2’s ability in VCOS. First, we assess SAM2’s performance on camouflaged video datasets using different models and prompts (click, box, and mask). Second, we explore the integration of SAM2 with existing multimodal large language models (MLLMs) and VCOS methods. Third, we specifically adapt SAM2 by fine-tuning it on the video camouflaged dataset. Our comprehensive experiments demonstrate that SAM2 has the excellent zero-shot ability to detect camouflaged objects in videos. We also show that this ability could be further improved by specifically adjusting SAM2’s parameters for VCOS.
  • An empirical study of LLaMA3 quantization: from LLMs to MLLMs
    Item type: Journal Article
    Huang, Wei; Zheng, Xingyu; Ma, Xudong; et al. (2024)
    Visual Intelligence
    The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3’s capabilities when quantized to low bit-width. This exploration can potentially provide new insights and challenges for the low-bit quantization of LLaMA3 and other future LLMs, especially in addressing performance degradation issues that suffer in LLM compression. Specifically, we comprehensively evaluate the 10 existing post-training quantization and LoRA fine-tuning (LoRA-FT) methods of LLaMA3 on 1-8 bits and various datasets to reveal the low-bit quantization performance of LLaMA3. To uncover the capabilities of low-bit quantized MLLM, we assessed the performance of the LLaMA3-based LLaVA-Next-8B model under 2-4 ultra-low bits with post-training quantization methods. Our experimental results indicate that LLaMA3 still suffers from non-negligible degradation in linguistic and visual contexts, particularly under ultra-low bit widths. This highlights the significant performance gap at low bit-width that needs to be addressed in future developments. We expect that this empirical study will prove valuable in advancing future models, driving LLMs and MLLMs to achieve higher accuracy at lower bit to enhance practicality.
Publications 1 - 2 of 2