57 accepted papers
Vericoding refers to the generation of formally verified code from rigorous specifications. Recent AI models show promise in vericoding, but a unified methodology for cross-paradigm evaluation is lacking. Existing benchmarks test only an individual language/tool (e.g., Dafny, Verus, and Lean) and ea…
As frontier Large Language Models (LLMs) increasingly saturate new benchmarks shortly after they are published, benchmarking itself is at a juncture: if frontier models keep improving, it will become increasingly hard for humans to generate discriminative tasks, provide accurate ground-truth answers…
Zero-Shot Relation Triplet Extraction (ZSRTE) aims to extract relation triplets for unseen relation types without any annotated training data. Recent advancements in Large Language Models (LLMs) have significantly enhanced ZSRTE performance, enabling the direct generation of relational triplets from
Stereo image compression is essential for a wide range of 3D vision. Recent methods have demonstrated strong capabilities in eliminating inter-view redundancy and enabling compact entropy coding via spatial-domain stereo transformation and advanced autoregressive entropy models. However, these appro
The heat transfer tubes of the steam generator are critical components of the nuclear power system and require regular inspection to ensure safety. The SG-Climbot, a quadruped heat transfer tube inspection robot, is equipped with a guiding device capable of simultaneously aligning with and inspectin…
With the widespread application of Video Diffusion Models (VDMs), video synthesis has achieved remarkable temporal dynamics. Image-to-Video (I2V) generation allows users to provide reference images, which enables attackers to inject adversarial noise into these conditions. Due to the robust spatio-t…
Stereo image compression (SIC) has become increasingly vital with its applications surging in fields such as 3D reconstruction and autonomous navigation. Previous methods leverage cross-attention to model inter-view redundancy and employ autoregressive entropy models to predict probability distribut
Visual manipulation localization (VML) aims to identify tampered regions in images and videos, a task that has become increasingly challenging with the rise of advanced editing tools. Existing methods face two main issues: resolution diversity, where resizing or padding distorts forensic traces and…
Large language models have enabled sophisticated dialogue planning policy, but their reliance on LLM-generated simulation and feedback for policy optimization may introduce systematic preference bias. We present the first comprehensive analysis of preference bias in LLM-based dialogue planners, eval
We study reward poisoning attacks in reinforcement learning (RL), where an adversary manipulates rewards within constrained budgets to force the target RL agent to adopt a policy that aligns with the attacker's objectives. Prior works on reward poisoning mainly focused on sufficient conditions to de…
Infrared and visible image fusion (IVIF) is a crucial technique for enhancing visual performance by integrating unique information from different modalities into one fused image. Exiting methods pay more attention to conducting fusion with undisturbed data, while overlooking the impact of deliberate…
Knowledge Graph Question Answering (KGQA) aims to answer natural language questions based on knowledge graphs.Recent approaches apply the Retrieval-Augmented Generation (RAG) paradigm to incorporate Large Language Models (LLMs) to this task, where a retriever selects a question-related subgraph and
While image conditional diffusion models demonstrate impressive generation capabilities, they exhibit high vulnerability when facing backdoor and adversarial attacks. In this paper, we define a scenario named diffusion anomaly where the generated results of a reverse process under attack deviate sig…
With the extensive deployment and application of Internet of Things (IoT) devices, vulnerable edge nodes have emerged as primary targets for Advanced Persistent Threat (APT) attacks. Attackers compromise IoT terminal devices to establish an initial foothold and subsequently exploit lateral movement
Large Language Models (LLMs) have shown impressive capabilities in language understanding and generation, leading to growing interest in zero-shot relation triplet extraction (ZeroRTE), a task that aims to extract triplets for unseen relations without annotated data. However, existing methods typica
Camera pose estimation is a fundamental computer vision task that is essential for applications like visual localization and multi-view stereo reconstruction. In the object-centric scenarios with sparse inputs, the accuracy of pose estimation can be significantly influenced by background textures th…
The heat transfer tubes of the steam generator are critical components of the nuclear power system and require regular inspection to ensure safety. The SG-Climbot, a quadruped heat transfer tube inspection robot, is equipped with a guiding device capable of simultaneously aligning with and inspectin
Vision-Language Pre-training (VLP) models have exhibited unprecedented capability in many applications by taking full advantage of the learned multimodal alignment. However, previous studies have shown they are vulnerable to maliciously crafted adversarial samples. Despite recent success, these atta…
Enhancing the intelligence of smart systems, such as smart homes, smart vehicles, and smart grids, critically depends on developing sophisticated planning capabilities that can anticipate the next desired function based on historical interactions. While existing methods view user behaviors as sequen…
As point clouds gain widespread application in fields such as autonomous driving and scene modeling, an increasing number of point cloud learning networks have emerged. As a result, research on 3D adversarial attacks and defenses has rapidly advanced. To the best of our knowledge, existing 3D defens…
Large Language Models (LLMs) have exhibited exceptional performance across a spectrum of natural language processing tasks. However, their substantial sizes pose considerable challenges, particularly in computational demands and inference speed, due to their quadratic complexity. In this work, we ha…
Text-to-Time Series generation holds significant potential to address challenges such as data sparsity, imbalance, and limited availability of multimodal time series data across domains. While diffusion models have achieved remarkable success in Text-to-X (e.g., vision and audio data) generation, th
Time series analysis plays a critical role in numerous applications, supporting tasks such as forecasting, classification, anomaly detection, and imputation. In this work, we present the time series pattern machine (TSPM), a model designed to excel in a broad range of time series tasks through power…
Although substantial efforts have been made to mitigate catastrophic forgetting in continual learning, the intrinsic mechanisms are not well understood. In this work, we demonstrate the existence of “pseudo forgetting”: the performance degradation in previous tasks is not attributed to a loss of cap…
We study reward poisoning attacks on Combinatorial Multi-armed Bandits (CMAB). We first provide a sufficient and necessary condition for the attackability of CMAB, a notion to capture the vulnerability and robustness of CMAB. The attackability condition depends on the intrinsic properties of the cor…
Data augmentation has been widely applied in anomaly detection, which generates synthetic anomalous data for training. However, most existing anomaly augmentation methods focus on image-level cut-and-paste techniques, resulting in less realistic synthetic results, and are restricted to a few predefi…
Center of pressure (COP) serves as a widely utilized indicator for evaluating balance-related issues, e.g., gait quality of neurological disorders, fall risk of the elderly, and recovery of the injured. Existing methods for acquiring COP mostly rely on expensive force platforms or wearable force-sen…
Large Language Models (LLMs) demonstrate significant value in domain-specific applications, benefiting from their fundamental capabilities. Nevertheless, it is still unclear which fundamental capabilities contribute to success in specific domains. Moreover, the existing benchmark-based evaluation ca…
Traditional QR codes consist of a grid of black-and-white square modules, which lack aesthetic appeal and meaning for human perception. This has motivated recent research to beautify the visual appearance of QR codes. However, there exists a trade-off between the visual quality and scanning-robustne…
"Mesh-based facial blendshapes have been widely used in animation pipelines, while recent advancements in neural geometry and appearance representations have enabled high-quality inverse rendering. Building upon these observations, we introduce a novel technique that reconstructs mesh-based blendsha…
Domain randomization introduces perturbations in the simulation to make controllers less susceptible to the reality gap, which enables remarkable sim-to-real transfer on real quadruped robots. However, aleatoric uncertainty originating from perturbations could often lead to suboptimal controllers. I
In this paper we present LiDAR-Net a new real-scanned indoor point cloud dataset containing nearly 3.6 billion precisely point-level annotated points covering an expansive area of 30000m^2. It encompasses three prevalent daily environments including learning scenes working scenes and living scenes.…
Reconstructing natural speech from neural activity is vital for enabling direct communication via brain-computer interfaces. Previous efforts have explored the conversion of neural recordings into speech using complex deep neural network (DNN) models trained on extensive neural recording data, which…
Language style is necessary for AI systems to accurately understand and generate diverse human language. However, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to mee…
Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach c…
Contrastive Language-Image Pre-training (CLIP) has significantly boosted the performance of various vision-language tasks by scaling up the dataset with image-text pairs collected from the web. However, the presence of intrinsic noise and unmatched image-text pairs in web data can potentially affect…
In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion models the latent space induced by an encoder and generates images through a paired decoder. Although the selection of the latent space is empirically pivotal, determining the optim…
Deep generative models have demonstrated superior performance in lossless compression on identically distributed data. However, in real-world scenarios, data to be compressed are of various distributions and usually cannot be known in advance. Thus, commercially expected neural compression must have…
Diffusion models have proven to be highly effective in generating high-quality images. However, adapting large pre-trained diffusion models to new domains remains an open challenge, which is critical for real-world applications. This paper proposes DiffFit, a parameter-efficient strategy to fine-tun…
In this work, we propose Fair-CDA, a fine-grained data augmentation strategy for imposing fairness constraints. We use a feature disentanglement method to extract the features highly related to the sensitive attributes. Then we show that group fairness can be achieved by regularizing the models on t…
Human hand has amazing super-resolution ability in sensing the force and position of contact and this ability can be strengthened by practice. Inspired by this, we propose a method for robotic tactile super-resolution enhancement by learning spatiotemporal continuity of contact position and a tactil…
Rewriting incomplete and ambiguous utterances can improve dialogue models’ understanding of the context and help them generate better results. However, the existing end-to-end models will have the problem of too large search space, resulting in poor quality of rewriting results. We propose a 2-phase…
Beyond achieving higher compression efficiency over classical image compression codecs, deep image compression is expected to be improved with additional side information, e.g., another image from a different perspective of the same scene. To better utilize the side information under the distributed…
Interpretable AI tools are often motivated by the goal of understanding model behavior in out-of-distribution (OOD) contexts. Despite the attention this area of study receives, there are comparatively few cases where these tools have identified previously unknown bugs in models. We argue that this i…
Deploying deep learning models to new environments is very challenging. Domain adaptation (DA) is a promising paradigm to solve the problem by collecting and adapting to unlabeled data in new environments. Though research efforts have led to steady performance improvement over the past decade, DA al…
Unsupervised surface anomaly detection aims at discovering and localizing anomalous patterns using only anomaly-free training samples. Reconstruction-based models are among the most popular and successful methods, which rely on the assumption that anomaly regions are more difficult to reconstruct. H…
Spatial-temporal local binary pattern (STLBP) has been widely used in dynamic texture recognition. STLBP often encounters the high-dimension problem as its dimension increases exponentially, so that STLBP could only utilize a small neighborhood. To tackle this problem, we propose a method for dynami…
Few-shot abstractive summarization has become a challenging task in natural language generation. To support it, we developed a novel soft prompts architecture coupled with a prompt pre-training plus prompt fine-tuning paradigm, which is effective and tunes only extremely light parameters. To meet th…
Almost all existing amodal segmentation methods make the inferences of occluded regions by using features corresponding to the whole image. This is against the human's amodal perception, where human uses the visible part and the shape prior knowledge of the target to infer the occluded region. To mi…
Financial signal processing is a matter of great concern in FinTech. Traditionally, recurrent networks are often used to model time series, while the latest research shows that convolutional networks, especially temporal convolutional networks (TCNs), are also powerful and effective for a large numb…
Automated data augmentation has shown superior performance in image recognition. Existing works search for dataset-level augmentation policies without considering individual sample variations, which are likely to be sub-optimal. On the other hand, learning different policies for different samples na…
Deep neural networks are susceptible to adversarially crafted, small, and imperceptible changes in the natural inputs. The most effective defense mechanism against these examples is adversarial training which constructs adversarial examples during training by iterative maximization of loss. The mode…
A polymatrix game is a multi-player game over n players, where each player chooses a pure strategy from a list of its own pure strategies. The utility of each player is a sum of payoffs it gains from the two player's game from all its neighbors, under its chosen strategy and that of its neighbor. As…
Instance segmentation can detect where the objects are in an image, but hard to understand the relationship between them. We pay attention to a typical relationship, relative saliency. A closely related task, salient object detection, predicts a binary map highlighting a visually salient region whil…
Face presentation attacks have become an increasingly critical issue in the face recognition community. Many face anti-spoofing methods have been proposed, but they cannot generalize well on "unseen" attacks. This work focuses on improving the generalization ability of face anti-spoofing methods fro…
Recently developed Dual Domain Image Denoising (DDID) algorithm is a simple version of block-matching 3D filtering (BM3D) by combining bilateral filter and frequency-based method. DDID and its invariants have achieved competitive results compared with state-of-the-art methods. However, this kind of…
Label estimation is an important component in an unsupervised person re-identification (re-ID) system. This paper focuses on cross-camera label estimation, which can be subsequently used in feature learning to learn robust re-ID models. Specifically, we propose to construct a graph for samples in ea…