24 accepted papers
Recent Vision-Language-Action (VLA) models reformulate vision-language models by tuning them with millions of robotic demonstrations. While they perform well when fine-tuned for a single embodiment or task family, extending them to multi-skill settings remains challenging: directly merging VLA exper
We introduce AMIA, a lightweight, inference-only defense for Large Vision–Language Models (LVLMs) that (1) Automatically Masks a small set of text-irrelevant image patches to disrupt adversarial perturbations, and (2) conducts joint Intention Analysis to uncover and mitigate hidden harmful intents b
This paper introduces Goku, a state-of-the-art family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance. We detail the foundational elements enabling high-quality visual generation, including the data curation pipeline, model ar…
We present Infinity, a Bitwise Visual AutoRegressive Modeling capable of generating high-resolution, photorealistic images following language instruction. Infinity refactors visual autoregressive model under a bitwise token prediction framework with an infinite-vocabulary classifier and bitwise self…
Aligning large language models (LLMs) with human values, particularly when facing complex and stealthy jailbreak attacks, presents a formidable challenge. Unfortunately, existing methods often overlook this intrinsic nature of jailbreaks, which limits their effectiveness in such complex scenarios. I…
3D Gaussian Splatting (3DGS) has gained significant attention for its real-time, photo-realistic rendering in novel-view synthesis and 3D modeling. However, existing methods struggle with accurately modeling scenes affected by transient objects, leading to artifacts in the rendered images. We identi…
This work identifies the *Energy Loss Phenomenon* in Reinforcement Learning from Human Feedback (RLHF) and its connection to reward hacking. Specifically, energy loss in the final layer of a Large Language Model (LLM) gradually increases during the RL process, with an *excessive* increase in energy…
Video advertising has become a popular marketing strategy on e-commerce platforms, requiring high-level semantic reasoning like selling point discovery, narrative organization. Previous rule-based methods struggle with these complex tasks, and learning-based approaches demand large datasets and high
We present a neural radiance field method for urban-scale semantic and building-level instance segmentation from aerial images by lifting noisy 2D labels to 3D. This is a challenging problem due to two primary reasons. Firstly objects in urban aerial images exhibit substantial variations in size inc…
Recent advancement in computer vision has significantly lowered the barriers to artistic creation. Exemplar-based image translation methods have attracted much attention due to flexibility and controllability. However, these methods hold assumptions regarding semantics or require semantic informatio…
Cross-lingual phrase retrieval aims to retrieve parallel phrases among languages. Current approaches only deals with textual modality. There lacks multimodal data resources and explorations for multimodal cross-lingual phrase retrieval (MXPR). In this paper, we create the first MXPR data resource an…
3D visual grounding plays a crucial role in scene understanding with extensive applications in AR/VR. Despite the significant progress made in recent methods the requirement of dense textual descriptions for each individual object which is time-consuming and costly hinders their scalability. To miti…
Lexically constrained neural machine translation (LCNMT), which controls the translation generation with pre-specified constraints, is important in many practical applications. Current approaches to LCNMT typically assume that the pre-specified lexicon constraints are contextually appropriate. This…
Machine translation technology has made great progress in recent years, but it cannot guarantee error-free results. Human translators perform post-editing on machine translations to correct errors in the scene of computer aided translation. In favor of expediting the post-editing process, many works…
Improving neural machine translation (NMT) systems with prompting has achieved significant progress in recent years. In this work, we focus on how to integrate multi-knowledge, multiple types of knowledge, into NMT models to enhance the performance with prompting. We propose a unified framework, whi…
Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation capacity. However, existing GCN-based methods buil…
Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many high-level inference and prediction tasks. How to make the system perform efficiently on a large number of cameras is a crucial research issue. Previous works have proposed many matching strategies to reduc…
Deep learning models for handwriting recognition have been developed in recent years. To improve the model’s generalization ability for sequence modeling task, this paper proposes to use domain adaptation with statistical distribution alignment and entropy regularization. For statistical distributio…
Nowadays, deep learning is widely applied to extract features for similarity computation in person re-identification (re-ID). However, the difference between the training data and testing data makes the performance of learned feature degraded during testing. Hence, re-ranking is proposed to mitigate…
Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e.g., clear edges, smooth color shadin…
Word alignment is to find translationally equivalent words between source and target sentences. Previous work has demonstrated that self-training can achieve competitive word alignment results. In this paper, we propose to use word alignments generated by a third-party word aligner to supervise the…
Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT). Traditionally, a QE system accepts the original source text and translation from a black-box MT system as input. Recently, a few studies indicate that as a by-product of translation, QE benefits from the mod…
Place is an important element in visual understanding. Given a photo of a building, people can often tell its functionality, e.g. a restaurant or a shop, its cultural style, e.g. Asian or European, as well as its economic type, e.g. industry oriented or tourism oriented. While place recognition has…
Relationships among objects play a crucial role in image understanding. Despite the great success of deep learning techniques in recognizing individual objects, reasoning about the relationships among objects remains a challenging task. Previous methods often treat this as a classification problem,…