9 accepted papers
While recent Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning abilities, they often produce ungrounded, hallucinated responses by over-relying on linguistic priors rather than visual evidence. This critical limitation arises from the lack of a quantitative measure of how
Fairness in machine learning has emerged as a central concern, as predictive models frequently inherit or even amplify biases present in training data. Such biases often manifest as unintended correlations between model outcomes and sensitive attributes, leading to systematic disparities across demo…
RAW images captured by different camera sensors exhibit substantial domain shifts due to varying spectral responses, noise characteristics, and tone behaviors, complicating their direct use in downstream computer vision tasks. Prior methods address this problem by training domain-specific RAW-to-RAW
Customizable image retrieval from large datasets remains a critical challenge, particularly when preserving spatial relationships within images. Traditional hashing methods, primarily based on deep learning, often fail to capture spatial information adequately and lack transparency. In this paper, w…
Referring multi-object tracking (RMOT) is an emerging cross-modal task that aims to localize an arbitrary number of targets based on a language expression and continuously track them in a video. This intricate task involves reasoning on multi-modal data and precise target localization with temporal…
We introduce Dr. RAW, a unified and tuning-efficient framework for high-level computer vision tasks directly operating on camera RAW data. Unlike previous approaches that optimize image signal processing (ISP) pipelines and fully fine-tune networks for each task, Dr. RAW achieves state-of-the-art pe…
Efficiency and performance are significant challenges in applying Machine Learning (ML) to robotics, especially in energy-constrained real-world scenarios. In this context, Hyperdimensional Computing offers an energy-efficient alternative but has been underexplored in robotics. We introduce ReactHD,…
This paper addresses the decomposition of holographic feature vectors in Hyperdimensional Computing (HDC) aka Vector Symbolic Architectures (VSA). HDC uses high-dimensional vectors with brain-like properties to represent symbolic information and leverages efficient operators to construct and manipul…
In this paper, we propose an efficient framework to accelerate a lightweight brain-inspired learning solution, hyperdimensional computing (HDC), on existing edge systems. Through algorithm-hardware co-design, we optimize the HDC models to run them on the low-power host CPU and machine learning accel…