22 accepted papers
Dataset Distillation (DD) seeks to create small, synthetic datasets for efficient model training. While diffusion models are powerful generators, their use in DD is hampered by distribution shifts between synthetic and ideal distilled data, leading to suboptimal performance. We identify two critical
Pre-training large language models (LLMs) on vast text corpora enhances natural language processing capabilities but risks encoding social biases, particularly gender bias. While parameter-modification methods like fine-tuning mitigate bias, they are resource-intensive, unsuitable for closed-source…
As deep learning advances, Large Language Models (LLMs) and their multimodal counterparts, Vision-Language Models (VLMs), have shown exceptional performance in many real-world tasks. However, VLMs face significant security challenges, such as jailbreak attacks, where attackers attempt to bypass the…
The issue of multimodal imbalance has attracted widespread attention recently, and spurred the proposal of numerous dataset-level modulation strategies. However, we observe that the degree of modality imbalance may vary significantly across different samples, suggesting that dataset-level strategies…
Tactile-aware robot learning faces critical challenges in data collection and representation due to data scarcity and sparsity, and the absence of force feedback in existing systems. To address these limitations, we introduce a tactile robot learning system with both hardware and algorithm innovatio…
Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the future of automated and personalized healthcare. However, the trustworthiness of Med-LVLMs remains unverified, posing s…
Recently dataset distillation has paved the way towards efficient machine learning especially for image datasets. However the distillation for videos characterized by an exclusive temporal dimension remains an underexplored domain. In this work we provide the first systematic study of video distilla…
"Data-efficient learning has garnered significant attention, especially given the current trend of large multi-modal models. Recently, dataset distillation has become an effective approach by synthesizing data samples that are essential for network training. However, it remains to be explored which…
Despite the striking success of general protein folding models such as AlphaFold2 (AF2), the accurate computational modeling of antibody-antigen complexes remains a challenging task. In this paper, we first analyze AF2's primary loss function, known as the Frame Aligned Point Error (FAPE), and raise…
Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to clean examples. With the wide applications of DNNs to Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), the vulnerability of SAR deep recognitio…
Prompt-based learning is a new language model training paradigm that adapts the Pre-trained Language Models (PLMs) to downstream tasks, which revitalizes the performance benchmarks across various natural language processing (NLP) tasks. Instead of using a fixed prompt template to fine-tune the model…
Though dataset distillation has witnessed rapid development in recent years, the distillation of multimodal data, e.g., image-text pairs, poses unique and under-explored challenges. Unlike unimodal data, image-text contrastive learning (ITC) data lack inherent categorization and should instead place…
"As a prominent research area, visual reasoning plays a crucial role in AI by facilitating concept formation and interaction with the world. However, current works are usually carried out separately on small datasets thus lacking generalization ability. Through rigorous evaluation of diverse benchma…
Understanding objects is a central building block of AI, especially for embodied AI. Even though object recognition excels with deep learning, current machines struggle to learn higher-level knowledge, e.g., what attributes an object has, and what we can do with it. Here, we propose a challenging Ob…
With the surge in attention to Egocentric Hand-Object Interaction (Ego-HOI), large-scale datasets such as Ego4D and EPIC-KITCHENS have been proposed. However, most current research is built on resources derived from third-person video action recognition. This inherent domain gap between first- and t…
In this paper, we propose an improved distributed fusion algorithm under the Labeled multi-Bernoulli (LMB) filter framework. Firstly, the LMB parameter set is augmented by a new group variable, which is able to record the matching information of the neighbour sensors. Then the matching LMB component…
"Long-tailed image recognition presents massive challenges to deep learning systems since the imbalance between majority (head) classes and minority (tail) classes severely skews the data-driven deep neural networks. Previous methods tackle with data imbalance from the viewpoints of data distributio…
Graph Convolutional Networks (GCNs) are powerful models for node representation learning tasks. However, the node representation in existing GCN models is usually generated by performing recursive neighborhood aggregation across multiple graph convolutional layers with certain sampling methods, whic…
Existing image-based activity understanding methods mainly adopt direct mapping, i.e. from image to activity concepts, which may encounter performance bottleneck since the huge gap. In light of this, we propose a new path: infer human part states first and then reason out the activities based on par…
Attributes and objects can compose diverse compositions. To model the compositional nature of these general concepts, it is a good choice to learn them through transformations, such as coupling and decoupling. However, complex transformations need to satisfy specific principles to guarantee the rati…
Vehicle re-identification (re-id) plays an important role in intelligent surveillance. Since difference vehicle models may have similar appearances, together with the problem of image scale variations, the vehicle re-id remains long-term challenging. We present a novel multi-scale vehicle re-id fram…
Gaussian process (GP) for machine learning has been well studied over the past two decades and is now widely used in many sectors. However, the design of low-complexity GP models still remains a challenging research problem. In this paper, we propose a novel scalable GP regression model for processi…