Panoptic narrative grounding (PNG), whose core target is fine-grained image-text alignment, requires a panoptic segmentation of referred objects given a narrative caption. Previous discriminative methods achieve only weak or coarse-grained alignment by panoptic segmentation pretraining or CLIP model adaptation. Given the recent progress of text-to-image Diffusion models, several works have shown their capability to achieve fine-grained image-text alignment through cross-attention maps and improved general segmentation performance. However, the direct use of phrase features as static prompts to apply frozen Diffusion models to the PNG task still suffers from a large task gap and insufficient vision-language interaction, yielding inferior performance. Therefore, we propose an Extractive-Injective Phrase Adapter (EIPA) bypass within the Diffusion UNet to dynamically update phrase prompts with image features and inject the multimodal cues back, which leverages the fine-grained image-text alignment capability of Diffusion models more sufficiently. In addition, we also design a Multi-Level Mutual Aggregation (MLMA) module to reciprocally fuse multi-level image and phrase features for segmentation refinement. Extensive experiments on the PNG benchmark show that our method achieves new state-of-the-art performance.

Zero-shot video recognition (ZSVR) is a task that aims to recognize video categories that have not been seen during the model training process. Recently, vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability for ZSVR. To make VLMs applicable to the video domain, existing methods often use an additional temporal learning module after the image-level encoder to learn the temporal relationships among video frames. Unfortunately, for video from unseen categories, we observe an abnormal phenomenon where the model that uses spatial-temporal feature performs much worse than the model that removes temporal learning module and uses only spatial feature. We conjecture that improper temporal modeling on video disrupts the spatial feature of the video. To verify our hypothesis, we propose Feature Factorization to retain the orthogonal temporal feature of the video and use interpolation to construct refined spatial-temporal feature. The model using appropriately refined spatial-temporal feature performs better than the one using only spatial feature, which verifies the effectiveness of the orthogonal temporal feature for the ZSVR task. Therefore, an Orthogonal Temporal Interpolation module is designed to learn a better refined spatial-temporal video feature during training. Additionally, a Matching Loss is introduced to improve the quality of the orthogonal temporal feature. We propose a model called OTI for ZSVR by employing orthogonal temporal interpolation and the matching loss based on VLMs. The ZSVR accuracies on popular video datasets (i.e., Kinetics-600, UCF101 and HMDB51) show that OTI outperforms the previous state-of-the-art method by a clear margin.Our codes are publicly available at https://github.com/yanzhu/mm2023_oti.

Due to the rapid growth of online video data, video-text retrieval techniques are in urgent need, which aim to search for the most relevant video given a natural language caption and vice versa. The major challenge of this task is how to identify the true fine-grained semantic correspondence between videos and texts, using only the document-level correspondence. To deal with this issue, we propose a simple yet effective two-stream framework which takes the concept information into account and introduces a new branch of semantic-level matching. We further propose a concept propagation mechanism for mining the latent semantics in videos and achieving enriched representations. The concept propagation is achieved by building a commonsense graph distilled from ConceptNet with concepts extracted from videos and captions. The original concepts of videos are detected by pretrained detectors as the initial concept representations. By conducting attentional graph reasoning on the commonsense graph with the guidance of external knowledge, we can extend some new concepts in a detector-free manner for further enriching the video representations. In addition, a propagated BCE loss is designed for supervising the concept propagation procedure. Common space learning is then constructed for cross-modal matching. We conduct extensive experiments on various baseline models and several benchmark datasets. Promising experimental results demonstrate the effectiveness and generalization ability of our method.

Image-text matching is a fundamental research topic bridging vision and language. Recent works use hard negative mining to capture the multiple correspondences between visual and textual domains. Unfortunately, the truly informative negative samples are quite sparse in the training data, which are hard to obtain only in a randomly sampled mini-batch. Motivated by causal inference, we aim to overcome this shortcoming by carefully analyzing the analogy between hard negative mining and causal effects optimizing. Further, we propose Counterfactual Matching (CFM) framework for more effective image-text correspondence mining. CFM contains three major components, \ie, Gradient-Guided Feature Selection for automatic casual factor identification, Self-Exploration for causal factor completeness, and Self-Adjustment for counterfactual sample synthesis. Compared with traditional hard negative mining, our method largely alleviates the over-fitting phenomenon and effectively captures the fine-grained correlations between image and text modality. We evaluate our CFM in combination with three state-of-the-art image-text matching architectures. Quantitative and qualitative experiments conducted on two publicly available datasets demonstrate its strong generality and effectiveness. Code is available at: https://github.com/weihao20/cfm.

Zero-shot video classification (ZSVC) that aims to recognize video classes that have never been seen during model training, has become a thriving research direction. ZSVC is achieved by building mappings between visual and semantic embeddings. Recently, ZSVC has been achieved by automatically mining the underlying objects in videos as attributes and incorporating external commonsense knowledge. However, the object mined from seen categories can not generalized to unseen ones. Besides, the category-object relationships are usually extracted from commonsense knowledge or word embedding, which is not consistent with video modality. To tackle these issues, we propose to mine associated objects and category-object relationships for each category from retrieved web images. The associated objects of all categories are employed as generic attributes and the mined category-object relationships could narrow the modality inconsistency for better knowledge transfer. Another issue of existing ZSVC methods is that the model sufficiently trained with labeled seen categories may not generalize well to distinct unseen categories. To encourage a more reliable transfer, we propose Task Similarity aware Representation Learning (TSRL). In TSRL, the similarity between seen categories and the unseen ones is estimated and used to regularize the model in an appropriate way. We construct a model for ZSVC based on the constructed attributes, the mined category-object relationships and the proposed TSRL. Experimental results on four public datasets, i.e., FCVID, UCF101, HMDB51 and Olympic Sports, show that our model performs favorably against state-of-the-art methods. Our codes are publicly available at https://github.com/junbaoZHUO/TSRL.