19 accepted papers
The rise of AI agents powered by large language models (LLMs) has transformed intelligent systems by enabling autonomous tool utilizing, reasoning, and action across diverse tasks. Despite this rapid progress, existing video summarization approaches primarily focus on feature extraction or frame-lev
Embodied World Models (EWMs) have emerged as a scalable and risk-free paradigm for evaluating Vision-Language-Action (VLA) systems. However, their reliability as evaluation benchmarks is often limited by the representation gap between low-dimensional actions and high-dimensional video synthesis. Thi…
Recent efforts have successfully scaled plain Vision Transformers (ViTs) to unprecedented sizes, ranging from 6B to 22B parameters. In contrast, their hierarchical counterparts have remained largely constrained to less than 1B parameters. To bridge this gap, we propose EHV, an efficient hierarchical
Remote sensing image segmentation is critical for a range of applications, including natural disaster monitoring and precision agriculture. Open-vocabulary segmentation enhances flexibility by removing fixed category constraints, enabling more fine-grained and adaptive scene understanding. Unlike CL
Inspired by Segment Anything 2, which generalizes segmentation from images to videos, we propose SAM2MOT—a novel segmentation-driven paradigm for multi-object tracking that breaks away from the conventional detection-association framework. In contrast to previous approaches that treat segmentation a
Recent advances in diffusion models have achieved remarkable success in isolated computer vision tasks such as text-to-image generation, depth estimation, and optical flow. However, these models are often restricted by a ``single-task-single-model
Pruning is a commonly employed technique for deep neural networks (DNNs) aiming at compressing the model size to reduce computational and memory costs during inference. In contrast to conventional neural networks, large language models (LLMs) pose a unique challenge regarding pruning efficiency due…
CLIP has shown promising performance across many short-text tasks in a zero-shot manner. However, limited by the input length of the text encoder, CLIP struggles on under-stream tasks with long-text inputs (>77 tokens). To remedy this issue, we propose FIX-CLIP, which includes three novel modules: (…
Diffusion models have achieved significant progress in both image and video generation while still suffering from huge computation costs. As an effective solution, rectified flow aims to rectify the diffusion process of diffusion models into a straight line for few-step and even one-step generation.…
Referring Image Segmentation (RIS) aims to segment the target object in an image given a natural language expression. While recent methods leverage pre-trained vision backbones and more training corpus to achieve impressive results, they predominantly focus on simple expressions—short, clear noun ph…
Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart lags behind due to the excessive training cost. To avert the training burden, we propose a training-free ControlVideo to produce high-quality videos based on the provided text prom…
Unsupervised domain adaptive semantic segmentation leverages synthetic data to train a segmentation model and transfers it to unlabeled real images. Due to the style difference, the transferred model suffers from the domain gap. Even worse, some classes exhibit the extreme domain gap, where the feat…
In this paper we introduce a new perspective for improving image restoration by removing degradation in the textual representations of a given degraded image. Intuitively restoration is much easier on text modality than image one. For example it can be easily conducted by removing degradation-relate…
Vision transformers (ViTs) have emerged as a prevalent architecture for vision tasks owing to their impressive performance. However, their complexity dramatically increases when handling long token sequences, particularly for dense prediction tasks that require high-resolution input. Notably, dense…
As a typical self-supervised learning strategy, Masked Image Modeling (MIM) is driven by recovering all masked patches from visible ones. However, patches from the same image are highly correlated and it is redundant to reconstruct all the masked patches. We find that this redundancy is neglected by…
Recently, the Segment Anything Model (SAM) emerged as a powerful vision foundation model which is capable to segment anything in 2D images. This paper aims to generalize SAM to segment 3D objects. Rather than replicating the data acquisition and annotation procedure which is costly in 3D, we design…
Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS). Computing the Class Activation Map (CAM) from a multi-label classification network is the de facto paradigm for seed area generation, but CAMs generated from Convolutional Neural Networks (CNNs) and…
"Transformers have recently shown superior performance than CNN on semantic segmentation. However, previous works mostly focus on the deliberate design of the encoder, while seldom considering the decoder part. In this paper, we find that a light weighted decoder counts for segmentation, and propose…
"With the development of generative-based self-supervised learning (SSL) approaches like BeiT and MAE, how to learn good representations by masking random patches of the input image and reconstructing the missing information has grown in concern. However, BeiT and PeCo need a “pre-pretraining” stage…