24 accepted papers
Existing paradigms for remote sensing change detection are caught in a trade-off: CNNs excel at efficiency but lack global context, while Transformers capture long-range dependencies at a prohibitive computational cost. This paper introduces ChangeRWKV, a new architecture that reconciles this confli
Existing parameter isolation-based methods in continual learning employ diverse designs to learn more tasks within a limited model capacity. However, most of their designs inevitably incur substantial computational overhead if their model capacity is enlarged to accommodate further tasks as the task…
Modern vision models must capture image-level context without sacrificing local detail while remaining computationally affordable. We revisit this tradeoff and advance a simple principle: decouple the roles of global reasoning and local representation. To operationalize this principle, we introduce
Federated Learning (FL) enables collaborative training of distributed clients while protecting privacy. To enhance generalization capability in FL, prototype-based FL is in the spotlight, since shared global prototypes offer semantic anchors for aligning client-specific local prototypes. However, ex…
Image tokenization has significantly advanced visual generation and multimodal modeling, particularly when paired with autoregressive models. However, current methods face challenges in balancing efficiency and quality: high-resolution image generation either requires an excessive number of tokens o
Autonomous navigation in unknown environments requires multi-scale spatial understanding that captures geometric details, topological connectivity, and global structure to support high-level decision making under partial observability. Existing approaches struggle to efficiently capture such multi-s
Current fMRI decoders face a performance-fidelity trade-off where efficient ID encoders outperform geometrically-aligned surface-based models. We argue this is an artifact of inefficient surface tokenization and the failure to use anatomy as a predictive signal. We present **NeurIPS**, a framework t…
The increasing demand for real-time interaction in online video scenarios necessitates a new class of efficient streaming video understanding models. However, existing approaches often rely on a flawed, query-agnostic ``change-is-important'' principle, which conflates visual dynamics with semantic r…
In this paper, we present \textbf{\textit{FasterCache}}, a novel training-free strategy designed to accelerate the inference of video diffusion models with high-quality generation. By analyzing existing cache-based methods, we observe that \textit{directly reusing adjacent-step features degrades vid…
Posters serve an essential function in marketing and advertising by improving visual communication and brand visibility, thus significantly contributing to industrial design. With the latest developments in controllable T2I diffusion models, research interest has surged in text rendering within synt…
Despite significant progress in Video Large Language Models (Video-LLMs) for offline video understanding, existing online Video-LLMs typically struggle to simultaneously process continuous frame-by-frame inputs and determine optimal response timing, often compromising real-time responsiveness and na…
Language-Visual Large Models (LVLMs) have made significant strides in enhancing visual understanding capabilities. However, these models often struggle with knowledge-based visual tasks due to constrains in their pre-training data scope and timeliness. Existing Retrieval-Augmented Generation (RAG) m
Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant infor…
Performing better text translation by integrating auxiliary inputs from visual information has gained widespread attention in recent years. While existing methods outperform the text-only translation models, the step-by-step generative style reduces the inference speed, which limits their applicabil…
Despite the significant advancements of Large Vision-Language Models (LVLMs) on established benchmarks, there remains a notable gap in suitable evaluation regarding their applicability in the emerging domain of long-context streaming video understanding. Current benchmarks for video understanding ty…
Text-to-image (T2I) models are well known for their ability to produce highly realistic images, while multimodal large language models (MLLMs) are renowned for their proficiency in understanding and integrating multiple modalities. However, currently there is no straightforward and efficient framewo…
Despite advances in generating fluent texts, existing pretraining models tend to attach incoherent event sequences to involved entities when generating narratives such as stories and news. We conjecture that such issues result from representing entities as static embeddings of superficial words, whi…
Despite the success of text-to-text pre-trained models in various natural language generation (NLG) tasks, the generation performance is largely restricted by the number of labeled data in downstream tasks, particularly in data-to-text generation tasks. Existing works mostly utilize abundant unlabel…
Graph-level anomaly detection aims to distinguish anomalous graphs in a graph dataset from normal graphs. Anomalous graphs represent a very few but essential patterns in the real world. The anomalous property of a graph may be referable to its anomalous attributes of particular nodes and anomalous s…
Although Transformers with fully connected self-attentions are powerful to model long-term dependencies, they are struggling to scale to long texts with thousands of words in language modeling. One of the solutions is to equip the model with a recurrence memory. However, existing approaches directly…
The development of intelligent drones has simplified aerial filming and provided smarter assistant tools for users to capture a film-look footage. Existing methods of autonomous aerial filming either specify predefined camera movements for a drone to capture a footage, or employ heuristic approaches…
We investigate the problem of 6 degrees of freedom (DOF) camera planning for filming professional human motion videos using a camera drone. Existing methods either plan motions for only a pan-tilt-zoom (PTZ) camera, or adopt ad-hoc solutions without carefully considering the impact of video content…
Drones are enabling new forms of cinematography. Aerial filming via drones in action scenes is difficult because it requires users to understand the dynamic scenarios and operate the drone and camera simultaneously. Existing systems allow the user to manually specify the shots and guide the drone to…
Aerial filming in action scenes using a drone is difficult for inexperienced flyers because manipulating a remote controller and meeting the desired image composition are two independent, while concurrent, tasks. Existing systems attempt to utilize wearable GPS-based or infrared-based sensors to tra…