Audio Descriptions (ADs) aim to provide a narration of a movie in text form, describing non-dialogue-related narratives, such as characters, actions, or scene establishment. Automatic generation of ADs remains challenging due to: i) the domain gap between movie-AD data and existing data used to train vision-language models, and ii) the issue of contextual redundancy arising from highly similar neighboring visual clips in a long movie. In this work, we propose DistinctAD, a novel two-stage framework for generating ADs that emphasize distinctiveness to produce better narratives. To address the domain gap, we introduce a CLIP-AD adaptation strategy that does not require additional AD corpora, enabling more effective alignment between movie and AD modalities at both global and finegrained levels. In Stage-II, DistinctAD incorporates two key innovations: (i) a Contextual Expectation-Maximization Attention (EMA) module that reduces redundancy by extracting common bases from consecutive video clips, and (ii) an explicit distinctive word prediction loss that filters out repeated words in the context, ensuring the prediction of unique terms specific to the current AD. Comprehensive evaluations on MAD-Eval, CMD-AD, and TV-AD benchmarks demonstrate the superiority of DistinctAD, with the model consistently outperforming baselines, particularly in Recall@k/N, highlighting its effectiveness in producing high-quality, distinctive ADs.

Most existing text-video retrieval methods focus on cross-modal matching between the visual content of videos and textual query sentences. However, in real-world scenarios, online videos are often accompanied by relevant text information such as titles, tags, and even subtitles, which can be utilized to match textual queries. This insight has motivated us to propose a novel approach to text-video retrieval, where we directly generate associated captions from videos using zero-shot video captioning with knowledge from web-scale pre-trained models (e.g., CLIP and GPT-2). Given the generated captions, a natural question arises: what benefits do they bring to text-video retrieval? To answer this, we introduce Cap4Video, a new framework that leverages captions in three ways: i) Input data: video-caption pairs can augment the training data. ii) Intermediate feature interaction: we perform cross-modal feature interaction between the video and caption to produce enhanced video representations. iii) Output score: the Query-Caption matching branch can complement the original Query-Video matching branch for text-video retrieval. We conduct comprehensive ablation studies to demonstrate the effectiveness of our approach. Without any post-processing, Cap4Video achieves state-of-the-art performance on four standard text-video retrieval benchmarks: MSR-VTT (51.4%), VATEX (66.6%), MSVD (51.8%), and DiDeMo (52.0%). The code is available at https://github.com/whwu95/Cap4Video.

With the explosive growth of web videos and emerging large-scale vision-language pre-training models, e.g., CLIP, retrieving videos of interest with text instructions has attracted increasing attention. A common practice is to transfer text-video pairs to the same embedding space and craft cross-modal interactions with certain entities in specific granularities for semantic correspondence. Unfortunately, the intrinsic uncertainties of optimal entity combinations in appropriate granularities for cross-modal queries are understudied, which is especially critical for modalities with hierarchical semantics, e.g., video, text, etc. In this paper, we propose an Uncertainty-Adaptive Text-Video Retrieval approach, termed UATVR, which models each lookup as a distribution matching procedure. Concretely, we add additional learnable tokens in the encoders to adaptively aggregate multi-grained semantics for flexible high-level reasoning. In the refined embedding space, we represent text-video pairs as probabilistic distributions where prototypes are sampled for matching evaluation. Comprehensive experiments on four benchmarks justify the superiority of our UATVR, which achieves new state-of-the-art results on MSR-VTT (50.8%), VATEX (64.5%), MSVD (49.7%), and DiDeMo (45.8%). The code is available at https://github.com/bofang98/UATVR.

Recently, generative Text-based visual question answering (TextVQA) methods, which are often based on language models, have exhibited impressive results and drawn increasing attention. However, due to the inconsistencies in both input forms and optimization objectives, the power of pretrained language models is not fully explored, resulting in the need for large amounts of training data. In this work, we rethink the characteristics of the TextVQA task and find that scene text is indeed a special kind of language embedded in images. To this end, we propose a text-centered generative framework FITB (stands for Filling In The Blank), in which multimodal information is mainly represented in textual form and rationale-augmented prompting is involved. Specifically, an infilling-based prompt strategy is utilized to formulate TextVQA as a novel problem of filling in the blank with proper scene text according to the language context. Furthermore, aiming to prevent the model from language bias overfitting, we design a rough answer grounding module to provide visual rationales for promoting multimodal reasoning. Extensive experiments verify the superiority of FITB in both fully-supervised and zero-shot/few-shot settings. Notably, even with a saving of about 64M data, FITB surpasses the state-of-the-art method by 3.00% and 1.99% on TextVQA and ST-VQA datasets, respectively.

Contrastive self-supervised learning (CSL) has remarkably promoted the progress of visual representation learning. However, existing video CSL methods mainly focus on clip-level temporal semantic consistency. The temporal and spatial semantic correspondence across different granularities, i.e., video, clip, and frame levels, is typically overlooked. To tackle this issue, we propose a self-supervised Macro-to-Micro Semantic Correspondence (MaMiCo) learning framework, pursuing fine-grained spatiotemporal representations from a macro-to-micro perspective. Specifically, MaMiCo constructs a multiple branch architecture of T-MaMiCo and S-MaMiCo on a temporally-nested clip pyramid (video-to-frame). On the pyramid, T-MaMiCo aims at temporal correspondence by simultaneously assimilating semantic invariance representations and retaining appearance dynamics in long temporal ranges. For spatial correspondence, S-MaMiCo perceives subtle motion cues via ameliorating dense CSL for videos where stationary clips are applied for stably dense contrasting reference to alleviate semantic inconsistency caused by ''mismatching''. Extensive experiments justify that MaMiCo learns rich general video representations and works well on various downstream tasks, e.g., (fine-grained) action recognition, action localization, and video retrieval.

Existing video self-supervised learning methods mainly rely on trimmed videos for model training. They apply their methods and verify the effectiveness on trimmed video datasets including UCF101 and Kinetics-400, among others. However, trimmed datasets are manually annotated from untrimmed videos. In this sense, these methods are not truly unsupervised. In this article, we propose a novel self-supervised method, referred to as Exploring Relations in Untrimmed Videos (ERUV), which can be straightforwardly applied to untrimmed videos (real unlabeled) to learn spatio-temporal features. ERUV first generates single-shot videos by shot change detection. After that, some designed sampling strategies are used to model relations for video clips. The strategies are saved as our self-supervision signals. Finally, the network learns representations by predicting the category of relations between the video clips. ERUV is able to compare the differences and similarities of video clips, which is also an essential procedure for video-related tasks. We validate our learned models with action recognition, video retrieval, and action similarity labeling tasks with four kinds of 3D convolutional neural networks. Experimental results show that ERUV is able to learn richer representations with untrimmed videos, and it outperforms state-of-the-art self-supervised methods with significant margins.