This paper introduces CameraCtrl II, a framework that enables large-scale dynamic scene exploration through a camera-controlled video diffusion model. Previous camera-conditioned video generative models suffer from diminished video dynamics and limited range of viewpoints when generating videos with large camera movement. We take an approach that progressively expands the generation of dynamic scenes -- first enhancing dynamic content within individual video clip, then extending this capability to create seamless explorations across broad viewpoint ranges. Specifically, we construct a dataset featuring a large degree of dynamics with camera parameter annotations for training while designing a lightweight camera injection module and training scheme to preserve dynamics of the pretrained models. Building on these improved single-clip techniques, we enable extended scene exploration by allowing users to iteratively specify camera trajectories for generating coherent video sequences. Experiments across diverse scenarios demonstrate that CameraCtrl Ii enables camera-controlled dynamic scene synthesis with substantially wider spatial exploration than previous approaches.

Recent video generation models have shown promising results in producing high-quality video clips lasting several seconds. However, these models face challenges in generating long sequences that convey clear and informative events, limiting their ability to support coherent narrations. In this paper, we present a large-scale cooking video dataset designed to advance long-form narrative generation in the cooking domain. We validate the quality of our proposed dataset in terms of visual fidelity and textual caption accuracy using state-of-the-art Vision-Language Models (VLMs) and video generation models, respectively. We further introduce a Long Narrative Video Director to enhance both visual and semantic coherence in generated videos and emphasize the role of aligning visual embeddings to achieve improved overall video quality. Our method demonstrates substantial improvements in generating visually detailed and semantically aligned keyframes, supported by finetuning techniques that integrate text and image embeddings within the video generation process. Project page: https://videoauteur.github.io/

Ruohong Zhang, Liangke Gui, Zhiqing Sun, Yihao Feng, Keyang Xu, Yuanhan Zhang, Di Fu, Chunyuan Li, Alexander G Hauptmann, Yonatan Bisk, Yiming Yang. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Liangke Gui, Borui Wang, Qiuyuan Huang, Alexander Hauptmann, Yonatan Bisk, Jianfeng Gao. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Learning to hallucinate additional examples has recently been shown as a promising direction to address few-shot learning tasks. This work investigates two important yet overlooked natural supervision signals for guiding the hallucination process – (i) extrinsic: classifiers trained on hallucinated examples should be close to strong classifiers that would be learned from a large amount of real examples; and (ii) intrinsic: clusters of hallucinated and real examples belonging to the same class should be pulled together, while simultaneously pushing apart clusters of hallucinated and real examples from different classes. We achieve (i) by introducing an additional mentor model on data-abundant base classes for directing the hallucinator, and achieve (ii) by performing contrastive learning between hallucinated and real examples. As a general, model-agnostic framework, our dual mentor-and self-directed (DMAS) hallucinator significantly improves few-shot learning performance on widely-used benchmarks in various scenarios.

Deep convolutional neural networks (CNNs) have recognized promise as universal representations for various image recognition tasks. One of their properties is the ability to transfer knowledge from a large annotated source dataset (e.g., ImageNet) to a (typically smaller) target dataset. This is usually accomplished through supervised fine-tuning on labeled new target data. In this work, we address "unsupervised fine-tuning" that transfers a pre-trained network to target tasks with unlabeled data such as image clustering tasks. To this end, we introduce group-sparse non-negative matrix factorization (GSNMF), a variant of NMF, to identify a rich set of high-level latent variables that are informative on the target task. The resulting "factorized convolutional network" (FCN) can itself be seen as a feed-forward model that combines CNN and two-layer structured NMF. We empirically validate our approach and demonstrate state-of-the-art image clustering performance on challenging scene (MIT-67) and fine-grained (Birds-200, Flowers-102) benchmarks. We further show that, when used as unsupervised initialization, our approach improves image classification performance as well.

In this work, we present a dynamic response spoken dialogue system (DRSDS). It is capable of understanding the verbal and nonverbal language of users and making instant, situation-aware response. Incorporating with two external systems, MultiSense and email summarization, we built an email reading agent on mobile device to show the functionality of DRSDS.

Over the past few years, there has been an increased interest in machine understanding and recognition of affective states based on facial expressions. While great progress has been made, there are still a lot of challenges facing automatic emotion recognition, namely: generalizability of models across datasets, accounting for individual differences, and recognition of subtle expressions. While deep learning techniques enabled a large amount of progress in many areas of computer vision, this progress has not yet been fully translated to emotion recognition. Our work attempts to partly address that by presenting a novel learning technique for deep learning methods that leads to better generalization for emotion recognition from facial expressions.

The sheer amount of human-centric multimedia content has led to increased research on human behavior understanding. Most existing methods model behavioral sequences without considering the temporal saliency. This work is motivated by the psychological observation that temporally selective attention enables the human perceptual system to process the most relevant information. In this paper, we introduce a new approach, named Temporally Selective Attention Model (TSAM), designed to selectively attend to salient parts of human-centric video sequences. Our TSAM models learn to recognize affective and social states using a new loss function called speaker-distribution loss. Extensive experiments show that our model achieves the state-of-the-art performance on rapport detection and multimodal sentiment analysis. We also show that our speaker-distribution loss function can generalize to other computational models, improving the prediction performance of deep averaging network and Long Short Term Memory (LSTM).