42 accepted papers
Knowledge distillation from Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) is a prominent training paradigm. However, its efficacy is fundamentally limited by a spectral mismatch: SNNs, with their intrinsic low-pass filtering characteristics, struggle to learn high-frequency det
Simulation is essential to the development and evaluation of autonomous robots such as self-driving vehicles. Neural reconstruction is emerging as a promising solution as it enables simulating a wide variety of scenarios from real-world data alone in an automated and scalable way. However, while met
Generating accurate multilingual text with diffusion models has long been desired but remains challenging. Recent methods have made progress in rendering text in a single language, but rendering arbitrary languages is still an under-explored area. This paper introduces EasyText, a text rendering fra
Lookup table (LUT) methods demonstrate considerable potential in accelerating image super-resolution inference. However, pursuing higher image quality through larger receptive fields and bit-depth triggers exponential growth in the LUT's index space, creating a storage bottleneck that limits deploym…
Text-prompted image segmentation enables fine-grained visual understanding and is critical for applications such as human-computer interaction and robotics. However, existing supervised fine-tuning methods typically ignore explicit chain-of-thought (CoT) reasoning at test time, which limits their ab
We propose MagicQuill V2, a novel framework that introduces a layered composition paradigm to generative image editing, bridging the gap between the semantic power of modern diffusion models and the granular control of traditional graphics software. While state-of-the-art diffusion transformers exce
Spiking Neural Networks (SNNs) promise significant energy efficiency by processing information via sparse, event-driven spikes. However, realizing this potential is hindered by the conventional use of a rigid, uniform timestep, T. This constraint imposes a challenging trade-off between accuracy and
Playing video games requires perception, reasoning, memory, and long-horizon planning—exactly the faculties expected of modern large language and vision–language models (LLMs/VLMs). We introduce LMGame-Bench, a benchmark built on six popular games spanning platformer, puzzle, and narrative games thr…
We introduce ArtEditor, a novel framework for instruction-based image editing that learns unique editing styles from few-shot examples. While image editing has seen significant advancements, customized instructional editing remains underexplored. Existing methods often rely on complex, multi-stage p…
Cinematography is a cornerstone of film production and appreciation, shaping mood, emotion, and narrative through visual elements such as camera movement, shot composition, and lighting. Despite recent progress in multimodal large language models (MLLMs) and video generation models, the capacity of…
Generating realistic and controllable weather effects in videos is valuable for many applications. Physics-based weather simulation requires precise reconstructions that are hard to scale to in-the-wild videos, while current video editing often lacks realism and control.In this work, we introduce We…
Neural Radiance Fields and 3D Gaussian Splatting have revolutionized 3D reconstruction and novel-view synthesis task. However, achieving photorealistic rendering from extreme novel viewpoints remains challenging, as artifacts persist across representations. In this work, we introduce Difix3D+, a nov…
Recent advancements in Unet-based diffusion models, such as ControlNet and IP-Adapter, have introduced effective spatial and subject control mechanisms. However, the DiT (Diffusion Transformer) architecture still struggles with efficient and flexible control. To tackle this issue, we propose EasyCon…
Pixel grounding, encompassing tasks such as Referring Expression Segmentation (RES), has garnered considerable attention due to its potential for bridging the gap between vision and language modalities. However, advancements in this domain are currently constrained by limitations inherent in existin…
Time-series data are critical in diverse applications, such as industrial monitoring, medical diagnostics, and climate research. However, effectively integrating these high-dimensional temporal signals with natural language for dynamic, interactive tasks remains a significant challenge. To address t…
Social relationship recognition, as one of the fundamental tasks in video understanding, contributes to the construction and application of multi-modal knowledge graph. Previous works have mainly focused on two aspects: generating character graphs and multi-modal fusion. However, they often overlook
Out-of-distribution (OOD) fine-tuning has emerged as a promising approach for anomaly segmentation. Current OOD fine-tuning strategies typically employ global-level objectives, aiming to guide segmentation models to accurately predict a large number of anomaly pixels. However, these strategies often…
Adversarial attack and defense have been extensively explored in classification tasks, but their study in semantic segmentation remains limited. Moreover, current attacks fail to act as strong underlying attacks for adversarial training (AT), making it difficult to achieve segmentation robustness ag…
Learnable 3D Neural Graphics Representations (3DNGR) have emerged as promising 3D representations for reconstructing 3D scenes from 2D images. Numerous works, including Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and their variants, have significantly enhanced the quality of these r…
Current hair transfer methods struggle to handle diverse and intricate hairstyles, limiting their applicability in real-world scenarios. In this paper, we propose a novel diffusion-based hair transfer framework, named Stable-Hair, which robustly transfers a wide range of real-world hairstyles to use…
Enhancing defense through model ensemble is an emerging trend, where the challenge lies in how to use ensemble knowledge to counter Out-of-Distribution (OOD) attacks. In this paper, we propose the Reliable Defense Ensemble (REE) to address this issue. REE optimizes the ensemble knowledge of models t…
Recent years have witnessed remarkable advances in Large Language Models (LLMs). However, in the task of social relation recognition, Large Language Models (LLMs) encounter significant challenges due to their reliance on sequential training data, which inherently restricts their capacity to effectiv
In the field of multimodal learning, controlling the training of unimodal encoders from different perspectives is a primary approach to addressing Training Imbalance. However, the inherent capacity limitations of the modality affect the model’s capability. Therefore, generating adversarial examples…
Recent studies on Vision-Language-Action (VLA) models have shifted from the end-to-end action-generation paradigm toward a pipeline involving task planning followed by action generation, demonstrating improved performance on various complex, long-horizon manipulation tasks. However, existing approac…
Sparse phase retrieval aims to reconstruct an $n$-dimensional $k$-sparse signal from its phaseless measurements. For most of the existing reconstruction algorithms, their sampling complexity is known to be dominated by the initialization stage. In this paper, in order to improve the sampling complex…
Currently, personalized image generation methods mostly require considerable time to finetune and often overfit the concept resulting in generated images that are similar to custom concepts but difficult to edit by prompts. We propose an effective and fast approach that could balance the text-image…
UAVs and other fast moving robots often need to keep track of distant objects. Conventional zoom cameras commit to a particular viewpoint, and carrying multiple zoom cameras for multi-object tracking is not feasible for power limited robotic systems. We present a dual camera setup that allows tracki…
Great advancements in semantic, instance, and panoptic segmentation have been made in recent years, yet the top-performing models remain vulnerable to imperceptible adversarial perturbation. Current attacks on segmentation primarily focus on a single task, and these methods typically rely on iterati…
Simultaneous orthogonal matching pursuit (SOMP) is a classical algorithm for solving multiple measurement vectors (MMV) problems. In this paper, we analyze the theoretical performance of the SOMP algorithm using the restricted isometry property (RIP). In particular, we show that SOMP can robustly re…
Recent advancements in subject-driven image generation have led to zero-shot generation yet precise selection and focus on crucial subject representations remain challenging. Addressing this we introduce the SSR-Encoder a novel architecture designed for selectively capturing any subject from single…
Neutron computed tomography (nCT) is a 3D char-acterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined se…
This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL’s data heterogeneity issue. Specifically, we first analyze the effect of CS in FL and show that FL training can be accelerated by adequately choosing…
Carrying a load can affect human locomotion and increase the risk of musculoskeletal injuries. In this letter, we proposed a method inspired by the running posture of ungulate species to reduce metabolic energy by controlling load motion. We developed an energy-saving exoskeleton with an active load
Current few-shot segmentation (FSS) approaches have made tremendous achievements based on prototypical learning techniques. However, due to the scarcity of the support data provided, FSS methods still suffer from the intra-class and inter-class gaps. In this paper, we propose a uniform network to fi…
Active learning (AL) methods have been proven to be an effective way to reduce the labeling effort by intelligently selecting valuable instances for annotation. Despite their great success with in-distribution (ID) scenarios, AL methods suffer from performance degradation in many real-world applicat…
Modern mobile burst photography pipelines capture and merge a short sequence of frames to recover an enhanced image, but often disregard the 3D nature of the scene they capture, treating pixel motion between images as a 2D aggregation problem. We show that in a "long-burst", forty-two 12-megapixel R…
"Existing neural networks for computer vision tasks are vulnerable to adversarial attacks: adding imperceptible perturbations to the input images can fool these models to make a false prediction on an image that was correctly predicted without the perturbation. Various defense methods have proposed…
Current one-stage instance segmentation methods ignore the boundary information of masks, resulting in coarse masks that are far from the ground truth. In this paper, we propose a boundary-aware method to refine boundary information, called BSOLO. The core idea of BSOLO is to design a Hungarian-Algo…
Modern smartphones can continuously stream multi-megapixel RGB images at 60Hz, synchronized with high-quality 3D pose information and low-resolution LiDAR-driven depth estimates. During a snapshot photograph, the natural unsteadiness of the photographer's hands offers millimeter-scale variation in c…
We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-quality semantically segmented images requiring minimal human effort. Current deep networks are extremely data-hungry, benefiting from training on large-scale datasets, which are time-consuming to annotate. Our meth…
In Machine Learning as a Service, a provider trains a deep neural network and gives many users access. The hosted (source) model is susceptible to model stealing attacks, where an adversary derives a surrogate model from API access to the source model. For post hoc detection of such attacks, the pro…
Differentiable rendering has paved the way to training neural networks to perform “inverse graphics” tasks such as predicting 3D geometry from monocular photographs. To train high performing models, most of the current approaches rely on multi-view imagery which are not readily available in practice…