Multimodal Large Language Models (MLLMs) have garnered significant attention recently and demonstrate outstanding capabilities in various tasks such as OCR, VQA, captioning, etc. However, hallucination remains a persistent issue. While numerous methods have been proposed to mitigate hallucinations, achieving notable improvements, these methods primarily focus on mitigating hallucinations related to object/noun concepts. Verb concepts, which are crucial for understanding human actions, have been largely overlooked. In this paper, to the best of our knowledge, we are the first to investigate the verb hallucination phenomenon of MLLMs from various perspectives. Our findings reveal that most state-of-the-art MLLMs suffer from severe verb hallucination. To assess the effectiveness of existing mitigation methods for object concept hallucination in relation to verb hallucination, we evaluated these methods and found that they do not effectively address verb hallucination. To address this issue, we propose a baseline method based on fine-tuning with rich verb knowledge, achieving decent superiority. The experiment results demonstrate that our method significantly reduces hallucinations related to verbs.

Rectified-flow-based diffusion transformers like FLUX and OpenSora have demonstrated outstanding performance in the field of image and video generation. Despite their robust generative capabilities, these models often struggle with inversion inaccuracies, which could further limit their effectiveness in downstream tasks such as image and video editing. To address this issue, we propose RF-Solver, a novel training-free sampler that effectively enhances inversion precision by mitigating the errors in the ODE-solving process of rectified flow. Specifically, we derive the exact formulation of the rectified flow ODE and apply the high-order Taylor expansion to estimate its nonlinear components, significantly enhancing the precision of ODE solutions at each timestep. Building upon RF-Solver, we further propose RF-Edit, a general feature-sharing-based framework for image and video editing. By incorporating self-attention features from the inversion process into the editing process, RF-Edit effectively preserves the structural information of the source image or video while achieving high-quality editing results. Our approach is compatible with any pre-trained rectified-flow-based models for image and video tasks, requiring no additional training or optimization. Extensive experiments across generation, inversion, and editing tasks in both image and video modalities demonstrate the superiority and versatility of our method. The source code is available at https://github.com/wangjiangshan0725/RF-Solver-Edit.

Recent advances in Large Multi-modal Models (LMMs) have demonstrated their remarkable success as general-purpose multi-modal assistants, with particular focuses on holistic image- and video-language understanding. Conversely, less attention has been given to scaling fine-grained pixel-level understanding capabilities, where the models are expected to realize pixel-level alignment between visual signals and language semantics. Some previous studies have applied LMMs to related tasks such as region-level captioning and referring expression segmentation. However, these models are limited to performing either referring or segmentation tasks independently and fail to integrate these fine-grained perception capabilities into visual reasoning. To bridge this gap, we propose UniPixel, a large multi-modal model capable of flexibly comprehending visual prompt inputs and generating mask-grounded responses. Our model distinguishes itself by seamlessly integrating pixel-level perception with general visual understanding capabilities. Specifically, UniPixel processes visual prompts and generates relevant masks on demand, and performs subsequent reasoning conditioning on these intermediate pointers during inference, thereby enabling fine-grained pixel-level reasoning. The effectiveness of our approach has been verified on 10 benchmarks across a diverse set of tasks, including pixel-level referring/segmentation and object-centric understanding in images/videos. A novel PixelQA task that jointly requires referring, segmentation, and question answering is also designed to verify the flexibility of our method.

Dominant dual-encoder models enable efficient image-text retrieval but suffer from limited accuracy, while the cross-encoder models offer higher accuracy at the expense of efficiency. Distilling cross-modality matching knowledge from cross-encoder to dual-encoder provides a natural approach to harness their strengths. Thus, we investigate the following valuable question: how to make cross-encoder a good teacher for dual-encoder? Our findings are threefold: (1) Cross-modal similarity score distribution of cross-encoder is more concentrated, while the result of dual-encoder is nearly normal, making vanilla logit distillation less effective. However, ranking distillation remains practical, as it is not affected by the score distribution. (2) Only the relative order between hard negatives conveys valid knowledge, while the order information between easy negatives has little significance. (3) Maintaining the coordination between distillation loss and dual-encoder training loss is beneficial for knowledge transfer. Based on these findings, we propose a novel Contrastive Partial Ranking Distillation (CPRD) method, which implements the objective of mimicking relative order between hard negative samples with contrastive learning. This approach coordinates with the training of the dual-encoder, effectively transferring valid knowledge from the cross-encoder to the dual-encoder. Extensive experiments on image-text retrieval and ranking tasks show that our method surpasses other distillation methods and significantly improves the accuracy of dual-encoder.

Question-Driven Sign Language Translation (QSLT) addresses the challenge of translating sign language using pertinent questions in question-answering contexts. However, the pronounced modality complexity between question text and sign video poses a predicament: the model tends to overly depend on questions to generate translations, thereby neglecting the value of visual cues. To tackle this issue, the paper presents a Gloss-Bridged Translator (GBT), which introduces sign gloss as an intermediary conduit to establish semantic connections between questions and videos. By leveraging gloss, visual features are transformed into textual counterparts, mitigating the modality imbalance between these representations. Moreover, a cross-modal contrastive learning strategy is implemented, bolstering the global contextual relevance and local semantic alignment between questions and sign language. The proposed methodology is validated through extensive experiments on the proposed QSL dataset and other public sign language datasets. The results show the efficacy of integrating questions into sign language translation. The GBT yields remarkable improvements over prevailing SLT methods, attesting to its effectiveness and rationale. Our code and dataset is available athttps://github.com/glq-1992/QSL.

Although audio-visual representation has been proven to be applicable in many downstream tasks, the representation of dancing videos, which is more specific and always accompanied by music with complex auditory contents, remains challenging and uninvestigated. Considering the intrinsic alignment between the cadent movement of the dancer and music rhythm, we introduceMuDaR, a novelMusic-DanceRepresentation learning framework to perform the synchronization of music and dance rhythms both in explicit and implicit ways. Specifically, we derive the dance rhythms based on visual appearance and motion cues inspired by the music rhythm analysis. Then the visual rhythms are temporally aligned with the music counterparts, which are extracted by the amplitude of sound intensity. Meanwhile, we exploit the implicit coherence of rhythms implied in audio and visual streams by contrastive learning. The model learns the joint embedding by predicting the temporal consistency between audio-visual pairs. The music-dance representation, together with the capability of detecting audio and visual rhythms, can further be applied to three downstream tasks: (a) dance classification, (b) music-dance retrieval, and (c) music-dance retargeting. Extensive experiments demonstrate that our proposed framework outperforms other self-supervised methods by a large margin.

Current continuous sign language recognition systems generally target on a single language. When it comes to the multilingual problem, existing solutions often build separate models based on the same network and then train them with their corresponding sign language corpora. Observing that different sign languages share some low-level visual patterns, we argue that it is beneficial to optimize the recognition model in a collaborative way. With this motivation, we propose thefirstunified framework for multilingual continuous sign language recognition. Our framework consists of a shared visual encoder for visual information encoding, multiple language-dependent sequential modules for long-range temporal dependency learning aimed at different languages, and a universal sequential module to learn the commonality of all languages. An additional language embedding is introduced to distinguish different languages within the shared temporal encoders. Further, we present a max-probability decoding method to obtain the alignment between sign videos and sign words for visual encoder refinement. We evaluate our approach on three continuous sign language recognition benchmarks, i.e., RWTH-PHOENIX-Weather, CSL and GSL-SD. The experimental results reveal that our method outperforms the individually trained recognition models. Our method also demonstrates better performance compared with state-of-the-art algorithms.

Music-driven dance synthesis is a task to generate high-quality dance according to the music given by the user, which has promising entertainment applications. However, most of the existing methods cannot provide an efficient and effective way for user intervention in dance generation, e.g., posture-controllable. In this work, we propose a powerful framework named PC-Dance to perform adaptive posture-controllable music-driven dance synthesis. Consisting of an music-to-dance alignment embedding network (M2D-Align) and a posture-controllable dance synthesis (PC-Syn), PC-Dance allows fine-grained control by input anchor poses efficiently without artist participation. Specifically, to relieve the cost of artist participation but ensure generating high-quality dance efficiently, a self-supervised rhythm alignment module is designed to further learn the music-to-dance alignment embedding. As for PC-Syn, we introduce an efficient scheme for adaptive motion graph construction (AMGC), which could improve the efficiency of graph-based optimization and preserve the diversity of motions. Since there is few related public dataset, we collect an MMD-ARC dataset for music-driven dance synthesis. The experimental results on MMD-ARC dataset demonstrate the effectiveness of our framework and the feasibility for dance synthesis with adaptive posture controlling.

Despite existing pioneering works on sign language translation (SLT), there is a non-trivial obstacle, i.e., the limited quantity of parallel sign-text data. To tackle this parallel data bottleneck, we propose a sign back-translation (SignBT) approach, which incorporates massive spoken language texts into SLT training. With a text-to-gloss translation model, we first back-translate the monolingual text to its gloss sequence. Then, the paired sign sequence is generated by splicing pieces from an estimated gloss-to-sign bank at the feature level. Finally, the synthetic parallel data serves as a strong supplement for the end-to-end training of the encoder-decoder SLT framework.To promote the SLT research, we further contribute CSL-Daily, a large-scale continuous SLT dataset. It provides both spoken language translations and gloss-level annotations. The topic revolves around people’s daily lives (e.g., travel, shopping, medical care), the most likely SLT application scenario. Extensive experimental results and analysis of SLT methods are reported on CSL-Daily. With the proposed sign back-translation method, we obtain a substantial improvement over previous state-of-the-art SLT methods.

Sign language recognition (SLR) is a challenging problem, involving complex manual features (i.e., hand gestures) and fine-grained non-manual features (NMFs) (i.e., facial expression, mouth shapes, etc .). Although manual features are dominant, non-manual features also play an important role in the expression of a sign word. Specifically, many sign words convey different meanings due to non-manual features, even though they share the same hand gestures. This ambiguity introduces great challenges in the recognition of sign words. To tackle the above issue, we propose a simple yet effective architecture called Global-Local Enhancement Network (GLE-Net), including two mutually promoted streams toward different crucial aspects of SLR. Of the two streams, one captures the global contextual relationship, while the other stream captures the discriminative fine-grained cues. Moreover, due to the lack of datasets explicitly focusing on this kind of feature, we introduce the first non-manual-feature-aware isolated Chinese sign language dataset (NMFs-CSL) with a total vocabulary size of 1,067 sign words in daily life. Extensive experiments on NMFs-CSL and SLR500 datasets demonstrate the effectiveness of our method.

Continuous sign language recognition (SLR) deals with unaligned video-text pair and uses the word error rate (WER), i.e., edit distance, as the main evaluation metric. Since it is not differentiable, we usually instead optimize the learning model with the connectionist temporal classification (CTC) objective loss, which maximizes the posterior probability over the sequential alignment. Due to the optimization gap, the predicted sentence with the highest decoding probability may not be the best choice under the WER metric. To tackle this issue, we propose a novel architecture with cross modality augmentation. Specifically, we first augment cross-modal data by simulating the calculation procedure of WER, i.e., substitution, deletion and insertion on both text label and its corresponding video. With these real and generated pseudo video-text pairs, we propose multiple loss terms to minimize the cross modality distance between the video and ground truth label, and make the network distinguish the difference between real and pseudo modalities. The proposed framework can be easily extended to other existing CTC based continuous SLR architectures. Extensive experiments on two continuous SLR benchmarks, i.e., RWTH-PHOENIX-Weather and CSL, validate the effectiveness of our proposed method.

In this paper, we propose an alignment network with iterative optimization for weakly supervised continuous sign language recognition. Our framework consists of two modules: a 3D convolutional residual network (3D-ResNet) for feature learning and an encoder-decoder network with connectionist temporal classification (CTC) for sequence modelling. The above two modules are optimized in an alternate way. In the encoder-decoder sequence learning network, two decoders are included, i.e., LSTM decoder and CTC decoder. Both decoders are jointly trained by maximum likelihood criterion with a soft Dynamic Time Warping (soft-DTW) alignment constraint. The warping path, which indicates the possible alignment between input video clips and sign words, is used to fine-tune the 3D-ResNet as training labels with classification loss. After fine-tuning, the improved features are extracted for optimization of encoder-decoder sequence learning network in next iteration. The proposed algorithm is evaluated on two large scale continuous sign language recognition benchmarks, i.e., RWTH-PHOENIX-Weather and CSL. Experimental results demonstrate the effectiveness of our proposed method.

In this paper, we propose an approach to apply the Transformer with reinforcement learning (RL) for continuous sign language recognition (CSLR) task. The Transformer has an encoder-decoder structure, where the encoder network encodes the sign video into the context vector representation, while the decoder network generates the target sentence word by word based on the context vector. To avoid the intrinsic defects of supervised learning (SL) in our task, e.g., the exposure bias and non-differentiable task metrics issues, we propose to train the Transformer directly on non-differentiable metrics, i.e., word error rate (WER), through RL. Moreover, a policy gradient algorithm with baseline, which we call Self-critic REINFORCE, is employed to reduce variance while training. Experimental results on RWTH-PHOENIX-Weather benchmark verify the effectiveness of our method and demonstrate that our method achieves the comparable performance.

This paper presents a novel deep neural architecture with iterative optimization strategy for real-world continuous sign language recognition. Generally, a continuous sign language recognition system consists of visual input encoder for feature extraction and a sequence learning model to learn the correspondence between the input sequence and the output sentence-level labels. We use a 3D residual convolutional network (3D-ResNet) to extract visual features. After that, a stacked dilated convolutional network with Connectionist Temporal Classification (CTC) is applied for learning the mapping between the sequential features and the text sentence. The deep network is hard to train since the CTC loss has limited contribution to early CNN parameters. To alleviate this problem, we design an iterative optimization strategy to train our architecture. We generate pseudo-labels for video clips from sequence learning model with CTC, and fine-tune the 3D-ResNet with the supervision of pseudo-labels for a better feature representation. We alternately optimize feature extractor and sequence learning model with iterative steps. Experimental results on RWTH-PHOENIX-Weather, a large real-world continuous sign language recognition benchmark, demonstrate the advantages and effectiveness of our proposed method.

We propose a fast event retrieval method in video databases based on temporal match kernel. The basic idea of the baseline is to maximize the score function for all possible relative timestamps. However, considering the stability and computational cost, we simplify the similarity score by calculating the energy of the score function. In this way, maximizing the score function which is time-consuming and sensitive to noise is avoided. We derive the simplified energy formulation by using Parseval's theorem, which describes the unitarity of Fourier transform. Further, by our formulation, the problem becomes a simple nearest neighbor search, and we can use product quantization (PQ) to accelerate the computation. We evaluate our method on EVVE dataset for event retrieval. The experimental results show that our approach is much faster than the baseline, with a higher mAP as well. Comparison with state of the art demonstrates the efficacy of our approach.

Approximate Nearest Neighbour (ANN) search is an important research topic in multimedia and computer vision fields. In this paper, we propose a new deep supervised quantization method by Self-Organizing Map (SOM) to address this problem. Our method integrates the Convolutional Neural Networks (CNN) and Self-Organizing Map into a unified deep architecture. The overall training objective includes supervised quantization loss and classification loss. With the supervised quantization loss, we minimize the differences on the maps between similar image pairs, and maximize the differences on the maps between dissimilar image pairs. By optimization, the deep architecture can simultaneously extract deep features and quantize the features into the suitable nodes in the Self-Organizing Map. The experiments on several public standard datasets prove the superiority of our approach over the existing ANN search methods. Besides, as a byproduct, our deep architecture can be directly applied to classification task and visualization with little modification, and promising performances are demonstrated on these tasks in the experiments.

Sign Language Recognition (SLR) aims at translating the sign language into text or speech, so as to realize the communication between deaf-mute people and ordinary people. This paper proposes a framework based on the Hidden Markov Models (HMMs) benefited from the utilization of the trajectories and hand-shape features of the original sign videos, respectively. First, we propose a new trajectory feature (enhanced shape context), which can capture the spatio-temporal information well. Second, we fetch the hand regions by Kinect mapping functions and describe each frame by HOG (pre-processed by PCA). Moreover, in order to optimize predictions, rather than fixing the number of hidden states for each sign model, we independently determine it through the variation of the hand shapes. As for recognition, we propose a combination method to fuse the probabilities of trajectory and hand shape. At last, we evaluate our approach with our self-building Kinect-based dataset and the experiments demonstrate the effectiveness of our approach.