27 accepted papers
Human Action Recognition (HAR) is a fundamental computer vision task with diverse real-world applications. Practical deployments often involve low-light environments and unconstrained 6-DoF camera motion, conditions that degrade visual quality, disrupt temporal coherence, and compromise reliability
Multimodal Emotion Analysis (MEA) is crucial for human-centric AI, yet current methods struggle with two core challenges: the sparse nature of emotional cues across modalities and their inherent temporal asynchrony. Existing approaches, which often rely on implicit fusion, consequently suffer from d
Spatial transcriptomics (ST) measures gene expression at fine-grained spatial resolution, offering insights into tissue molecular landscapes. Previous methods for spatial gene expression prediction typically crop spots of interest from histopathology slide images, and train models to map each spot t
Graphical user interface (GUI) agents autonomously complete tasks across platforms (\eg, Linux) by sequentially decomposing user instructions into action proposals that iteratively interact with visual elements in the evolving environment. However, two main challenges arise: i) planning (\ie, the ac…
Co-segmentation aims to identify and segment common objects across a set of point clouds or images. Existing methods focus on single-modal co-segmentation. However, the limited semantics of a single modality restrict the discovery of common objects, leading to costly and labor-intensive segmentation
Nighttime photography is susceptible to flare caused by strong light sources, which degrades visual quality and disrupts structural information required by downstream vision tasks. Existing nighttime flare removal methods generally lack semantic priors for flare-occluded regions and thus tend to int
The spiking neuron model (SNM) mimics the processing paradigm of synaptic and membrane potentials in the cerebral cortex. However, existing SNMs are limited by two issues. First, they lack spike diversity. Although a spiking neuron perceives temporally varying input currents, SNMs only use identical
The challenge in LLM-based video understanding lies in preserving visual and semantic information in long videos while maintaining a memory-affordable token count. However, redundancy and correspondence in videos have hindered the performance potential of existing methods. Through statistical learni…
This paper studies zero-shot object recognition using event camera data. Guided by CLIP, which is pre-trained on RGB images, existing approaches achieve zero-shot object recognition by optimizing embedding similarities between event data and RGB images respectively encoded by an event encoder and th…
Neural networks derived from the M-P model have excelled in various visual tasks. However, as a simplified simulation version of the brain neural pathway, their structures are locked during training, causing over-fitting and over-parameterization. Although recent models have begun using the biomimet…
Solving expert-level multimodal tasks is a key milestone in general intelligence. As the capabilities of multimodal large language models (MLLMs) continue to evolve, evaluation of frontier multimodal intelligence becomes necessary yet challenging. In this work, we introduce ProBench, a benchmark of…
Fine-grained video action recognition can be formulated as a video–text matching problem. Previous approaches primarily rely on global video semantics to consolidate video embeddings, often leading to misaligned video–text pairs due to inaccurate atomic-level action understanding. This inaccuracy ar…
Optimization-based meta-learning methods for few-shot one-class classification (FS-OCC) aim to fine-tune a meta-trained model to classify the positive and negative samples using only a few positive samples by adaptation. However, recent approaches primarily focus on adjusting existing meta-learning…
In photography, an All-in-Focus (AiF) image may not always effectively convey the creator’s intent. Professional photographers manipulate Depth of Field (DoF) to control which regions appear sharp or blurred, achieving compelling artistic effects. For general users, the ability to flexibly adjust D…
Few-shot fine-grained human (FGH) action recognition is crucial in the context of human-robot interaction within open-set real-world environments. Existing works mainly focus on features extracted from RGB frames. However, their performances are drastically impacted in challenging scenarios, such as…
Recovering sharp images from dual-pixel (DP) pairs with disparity-dependent blur is a challenging task. Existing blur map-based deblurring methods have demonstrated promising results. In this paper we propose to the best of our knowledge the first framework to introduce the contrastive language-imag…
All-in-one (AiO) frameworks restore various adverse weather degradations with a single set of networks jointly. To handle various weather conditions an AiO framework is expected to adaptively learn weather-specific knowledge for different degradations and shared knowledge for common patterns. Howeve…
The artificial neuron (N-N) model-based networks have accomplished extraordinary success for various vision tasks. However, as a simplification of the mammal neuron model, their structure is locked during training, resulting in overfitting and over-parameters. The astrocyte, newly explored by biolog…
This paper proposes a pre-trained neural network for handling event camera data. Our model is a self-supervised learning framework, and uses paired event camera data and natural RGB images for training. Our method contains three modules connected in a sequence: i) a family of event data augmentation…
The dual-pixel (DP) sensor captures a two-view image pair in a single snapshot by splitting each pixel in half. The disparity occurs in defocus blurred regions between the two views of the DP pair, while the in-focus sharp regions have zero disparity. This motivates us to propose a K3DN framework fo…
With the concept of teaching being introduced to the machine learning community, a teacher model start using dynamic loss functions to teach the training of a student model. The dynamic intends to set adaptive loss functions to different phases of student model learning. In existing works, the teach…
The dual-pixel (DP) hardware works by splitting each pixel in half and creating an image pair in a single snapshot. Several works estimate depth/inverse depth by treating the DP pair as a stereo pair. However, dual-pixel disparity only occurs in image regions with the defocus blur. The heavy defocus…
Stereo camera systems play an important role in robotics applications to perceive the 3D world. However, conventional cameras have drawbacks such as low dynamic range, motion blur and latency due to the underlying frame- based mechanism. Event cameras address these limitations as they report the bri…
Event cameras are bio-inspired sensors that asynchronously report intensity changes in microsecond resolution. DAVIS can capture high dynamics of a scene and simultaneously output high temporal resolution events and low frame-rate intensity images. In this paper, we propose a single image (potential…
Event-based cameras can measure intensity changes (called 'events') with microsecond accuracy under high-speed motion and challenging lighting conditions. With the active pixel sensor (APS), the event camera allows simultaneous output of the intensity frames. However, the output images are captured…
The image motion blurring process is generally modelled as the convolution of a blur kernel with a latent image. Therefore, the estimation of the blur kernel is essentially important for blind image deblurring. Unlike existing approaches which focus on approaching the problem by enforcing various pr…
Videos for outdoor scene often show unpleasant blur effects due to the large relative motion between the camera and the dynamic objects and large depth variations. Existing works typically focus monocular video deblurring. In this paper, we propose a novel approach to deblurring from stereo videos.…