wcy1122/Long-TTS-Eval huggingface dataset:
Furthermore, we support reasoning-based generation in Mini-Gemini and empower current VLMs with any-to-any workflow. Extensive experiments on several zero-shot benchmarks prove the superiority of the proposed method, which surpasses previous leading approaches and even private models. We hope the Mini-Gemini can serve as a strong benchmark for image understanding and VLM-guided generation. Although Mini-Gemini achieves good results, it still has great potential to be further explored. For visual comprehension, the counting ability and complex visual reasoning ability are still far from satisfactory. This could be attributed to the lack of corresponding training data especially in the pretraining stage. Meanwhile, for reasoning-based generation, we use text to bridge the VLM and diffusion model in this work because we do not find apparent gain with embedding-based approaches. We will try to find a more advanced manner for visual understanding, reasoning, and generation.
China has become a tougher market. For global companies, China’s skilled labor force, extensive supplier ecosystem, and fast-growing domestic markets have long acted as a magnet for manufacturers. As labor costs have increased and worker productivity has declined, companies are struggling to find objective ways to gauge employee effectiveness. The COVID-19 pandemic broke traditional models of employment, says Leena Nair, former CHRO of Unilever. In all 15 countries surveyed by McKinsey, toxic workplace behavior was the biggest predictor of burnout symptoms and intent to leave—by a large margin. Under former president and CEO Mark Thompson’s watch, the New York Times’s digital readership jumped from half a million to nearly 5.7 million. Social media has four primary functions for businesses, including using social channels to respond to consumer requests. Generative AI is particularly good at generating personalized content and communication, due to its ability to analyze customer characteristics, behavior, and preferences.
In the case of the United Kingdom, however, it was the resolute accommodative monetary policy actions by the Bank of England that supported financial conditions after the referendum. Turning to the euro area, policy uncertainty increased in several countries in the first months of 2017. Looking ahead, further shocks stemming from the political sphere may, in the absence of offsetting factors, tighten domestic financial conditions, increase risk premia and potentially raise debt sustainability concerns. Excessive credit growth and leverage have been key drivers of past financial crises, notably the recent global financial crisis. For the appropriate setting of countercyclical macroprudential policy instruments, it is therefore important to identify periods of excessive credit developments at an early stage. This special feature discusses the standard statistical method for computing credit gaps and compares it with an alternative approach to measuring credit excesses based on fundamental economic factors. Theory-based credit gaps could provide a useful complement to statistical measures of cyclical systemic risk. Large stocks of non-performing loans (NPLs) on euro area bank balance sheets continue to present risks to financial stability. Significant legal and administrative reforms have been undertaken over recent years in countries with high levels of NPLs to streamline insolvency proceedings and maximise NPL recovery values. Yet, the market continues to provide low NPL valuations that result in wide bid-ask spreads, thus impeding large-scale NPL sales. This special feature highlights the potential role and benefits of co-investment strategies (between the private sector and the state) for addressing NPLs. These co-investment strategies may reduce information asymmetries between buyers and sellers, thereby enabling transactions that might otherwise not occur, or facilitate sales at higher prices. Moreover, the proposed schemes are priced at market levels and may, therefore, be free of state aid. The sectoral distribution of holdings of bank debt has a clear bearing on contagion and – by extension – on financial stability in the event of bank distress. Indeed, under the new bail-in regime in the EU, eventual write-downs (and/or conversion into equity) upon bank bail-in need to be distributed among shareholders and creditors according to a predefined creditor hierarchy, while avoiding contagion effects on the broader financial system. On the one hand, if a bank were to struggle, high financial sector concentration of its bail-inable debt could lead to concerns over spillover effects. On the other hand, if the bail-inable instruments were held mainly by the household sector, the use of bail-in tools in a bank resolution process may have negative effects on the economy resulting from effects on spending and potential political tensions. Financial distress in the non-bank financial sector can be transmitted to the banking sector through a number of direct and indirect transmission channels.
Hence, any fall in long-term interest rates may lead to a significant funding gap in balance sheets, given the typically longer average duration of liabilities than of assets. This Box discusses some of the financial stability issues raised by the impact of the low level of long-term interest rates on life insurers’ and pension funds’ balance sheets. As the so-called baby boom generations – i.e. those born between the mid-1940s and the mid1960s – approach retirement, policymakers in many developed countries have become increasingly concerned about retirement funding and retirement income security. With ongoing pension reforms reducing the generosity of funding from public sources, more emphasis is being placed on private saving. However, the inherent uncertainty about the length of human life complicates any decision regarding saving for retirement. In particular, there is a risk that individuals may outlive their resources and could be forced to reduce their living standards quite substantially when they reach a more advanced age, or even risk falling into a poverty trap. Longevity risk, which materialises when expectations regarding lifespan are not met, has two components.
While fake images were manually created long before software tools were available, generative models such as ours make the process easier. Fortunately, CNN-generated images currently have subtle flaws that allow detection, but improvements in generative models may make this more difficult. Generative models also reflect the biases in the datasets on which they are trained. As many large datasets are collected from the internet by automated systems, it can be difficult to remove these biases, especially when the images are unlabeled. If samples from generative models trained on these datasets proliferate throughout the internet, then these biases will only be reinforced further. On the other hand, diffusion models may be useful for data compression, which, as data becomes higher resolution and as global internet traffic increases, might be crucial to ensure accessibility of the internet to wide audiences. Our work might contribute to representation learning on unlabeled raw data for a large range of downstream tasks, from image classification to reinforcement learning, and diffusion models might also become viable for creative uses in art, photography, and music. Acknowledgments  This work was supported by ONR PECASE and the NSF Graduate Research Fellowship under grant number DGE-1752814. Google's TensorFlow Research Cloud (TFRC) provided Cloud TPUs.
We experimentally showed the ease of training and sampling when using the Flow Matching framework, and in the future, we expect FM to open the door to allowing a multitude of probability paths (for example, non-isotropic Gaussians or more general kernels altogether). Section 8: Social responsibility  Along side its many positive applications, image generation can also be used for harmful proposes. Using content-controlled training sets and image validation/classification can help reduce these uses. Furthermore, the energy demand for training large deep learning models is increasing at a rapid pace, focusing on methods that are able to train using less gradient updates or image throughput can lead to significant time and energy savings.
Replace—substituting one product for another sufficiently similar one. If tariffs increase prices, or make a product harder to find, buyers may switch to an alternative. For example, China imposed retaliatory tariffs on its imports of sorghum—mainly used as animal feed—from the United States in 2018. This induced livestock farmers to substitute sorghum with alternative grains such as corn. Ramp up—increasing the total amount of manufacturing or processing. Established manufacturers can build new factories, and new industries can develop (or old ones be revived). Ramp-up might occur in a low-tariff country seeking to export to the United States, or within the United States itself. For instance, when the United States imposed broad-based tariffs on China in 2018, production capacity in Vietnam ramped up, particularly linked to final assembly. US semiconductor production is currently ramping up, influenced by US tariffs together with domestic incentives and encouragement of foreign direct investment. Rearrange—shifting who imports from whom, without changing total production or the overall trade balance. For example, when the United States imposed a 25 percent tariff on truck tires from China in 2018, US imports from China dropped by over 60 percent—but total US truck tire imports remained stable, as exports from other economies rearranged toward the United States. For example, in 2023, China reported exporting $700 million worth of headphones to the United States, while the United States reported importing $3.3 billion worth of headphones from China. The rearrangement ratio varies a lot, depending on which number one chooses. In general, we have used importer-reported figures for the rearrangement ratio numerator, and exporter-reported figures for the denominator. However, this variation in trade flow values suggests the rearrangement ratio for most products can lie within plus or minus 0.2 of the values we report. Re-exports are sometimes misclassified as exports. Where possible, we have excluded re-exports from our sizing of the global available export market. However, some major re-export hubs feature in the trade data as the exporter, rather than re-exporter, of a product. For example, the Netherlands is often recorded as an exporter of goods that are in fact just transiting through the port at Rotterdam. This form of trade data distortion is often known as the “Rotterdam effect,” though this form of trade distortion is not unique to Rotterdam. As a result, the Netherlands features in trade data as the third-largest global exporter of laptops and the fifth-largest smartphone exporter. This phenomenon may inflate the size of the “available” global export market for some products. In addition, the interpretation of rearrangement ratios is subject to at least two important caveats, which, taken together, will bias some results toward underestimating the rearrangement ratio for a given product: Products within the same HS 6 level may not be substitutable. Although we use the most granular level of global trade data available, products classified under the same HS 6 classification may nevertheless not be substitutable.
Generative adversarial networks (GANs), autoregressive models, flows, and variational autoencoders (VAEs) have synthesized striking image and audio samples, and there have been remarkable advances in energy-based modeling and score matching that have produced images comparable to those of GANs. This paper presents progress in diffusion probabilistic models. A diffusion probabilistic model (which we will call a ``diffusion model'' for brevity) is a parameterized Markov chain trained using variational inference to produce samples matching the data after finite time. Transitions of this chain are learned to reverse a diffusion process, which is a Markov chain that gradually adds noise to the data in the opposite direction of sampling until signal is destroyed. When the diffusion consists of small amounts of Gaussian noise, it is sufficient to set the sampling chain transitions to conditional Gaussians too, allowing for a particularly simple neural network parameterization. Diffusion models are straightforward to define and efficient to train, but to the best of our knowledge, there has been no demonstration that they are capable of generating high quality samples. We show that diffusion models actually are capable of generating high quality samples, sometimes better than the published results on other types of generative models. In addition, we show that a certain parameterization of diffusion models reveals an equivalence with denoising score matching over multiple noise levels during training and with annealed Langevin dynamics during sampling. We obtained our best sample quality results using this parameterization, so we consider this equivalence to be one of our primary contributions. Despite their sample quality, our models do not have competitive log likelihoods compared to other likelihood-based models (our models do, however, have log likelihoods better than the large estimates annealed importance sampling has been reported to produce for energy based models and score matching). We find that the majority of our models' lossless codelengths are consumed to describe imperceptible image details. We present a more refined analysis of this phenomenon in the language of lossy compression, and we show that the sampling procedure of diffusion models is a type of progressive decoding that resembles autoregressive decoding along a bit ordering that vastly generalizes what is normally possible with autoregressive models. Section 2: Background
In part, this is due to ODE solvers being much more efficient---yielding lower error at similar computational costs---and the multitude of available ODE solver schemes. When compared to our ablation models, we find that models trained using Flow Matching with the OT path always result in the most efficient sampler, regardless of ODE solver, as demonstrated next. Sample paths. We first qualitatively visualize the difference in sampling paths between diffusion and OT. We find that the OT path model starts generating images sooner than the diffusion path models, where noise dominates the image until the very last time point. We additionally depict the probability density paths in 2D generation of a checkerboard pattern, noticing a similar trend. Low-cost samples. We next switch to fixed-step solvers and compare low (less than or equal to 100) NFE samples. We compare the per-pixel Mean Squared Error of low NFE solutions compared with 1000 NFE solutions, and notice that the FM with OT model produces the best numerical error, in terms of computational cost, requiring roughly only 60% of the NFEs to reach the same error threshold as diffusion models. Secondly, we show how FID changes as a result of the computational cost, where we find FM with OT is able to achieve decent FID even at very low NFE values, producing better trade-off between sample quality and cost compared to ablated models. Subsection 6.3: Conditional sampling from low-resolution images  Lastly, we experimented with Flow Matching for conditional image generation. In particular, upsampling images from 64x64 to 256x256. We follow the evaluation procedure in previous work and compute the FID of the upsampled validation images. Baselines include a reference FID of the original validation set, and a regression model. FM-OT achieves similar PSNR and SSIM values to previous work while considerably improving on FID and IS, which as argued by others is a better indication of generation quality. Section 7: Conclusion  We introduced Flow Matching, a new simulation-free framework for training Continuous Normalizing Flow models, relying on conditional constructions to effortlessly scale to very high dimensions. Furthermore, the FM framework provides an alternative view on diffusion models, and suggests forsaking the stochastic/diffusion construction in favor of more directly specifying the probability path, allowing us to, for example, construct paths that allow faster sampling and/or improve generation.
Using the E2E NLG Challenge dataset as an example, we report the validation loss and test metrics achieved by different choices of r after training for 26,000 steps. The optimal rank for GPT-2 Medium is between 4 and 16 depending on the metric used, which is similar to that for GPT-3 175B. Note that the relationship between model size and the optimal rank for adaptation is still an open question. Correlation between W and delta W  Note again that delta W does not contain the top singular directions of W, since the similarity between the top 4 directions in delta W and the top-10% of those in W barely exceeds 0.2. This gives evidence that delta W contains those "task-specific" directions that are otherwise not emphasized in W. An interesting next question to answer, is how "strong" do we need to amplify those task-specific directions, in order for the model adaptation to work well? Amplification Factor  One can naturally consider a feature amplification factor as the ratio of the Frobenius norm of delta W to the Frobenius norm of U transpose W V transpose, where U and V are the left- and right-singular matrices of the SVD decomposition of delta W. (Recall U U transpose W V transpose V gives the "projection" of W onto the subspace spanned by delta W.)  Intuitively, when delta W mostly contains task-specific directions, this quantity measures how much of them are amplified by delta W. As shown earlier, for r=4, this amplification factor is as large as 20. In other words, there are (generally speaking) four feature directions in each layer (out of the entire feature space from the pre-trained model W), that need to be amplified by a very large factor 20, in order to achieve our reported accuracy for the downstream specific task. And, one should expect a very different set of feature directions to be amplified for each different downstream task. One may notice, however, for r=64, this amplification factor is only around 2, meaning that most directions learned in delta W with r=64 are not being amplified by much. This should not be surprising, and in fact gives evidence (once again) that the intrinsic rank needed to represent the "task-specific directions" (thus for model adaptation) is low. In contrast, those directions in the rank-4 version of delta W (corresponding to r=4) are amplified by a much larger factor 20.
Here, ``streaming mode'' means the input text is received in a continuous flow rather than being known as a complete sentence in advance. In CosyVoice 2, the difference between streaming and non-streaming modes is only the way of sequence construction for LM. For the Non-Streaming mode, the ``start of sequence'' token, all text tokens, a ``turn of speech'' token, all speech tokens and the ``end of sequence'' token are concatenated sequentially.
However, additional aspects need consideration, such as the degree of visual content hallucination and fine-grained understanding of visual content. While text-only GPT-4 based multimodal evaluation is consistent and accurate in our study, its robustness in different situations and capability to evaluate other unexplored aspects are subjects for future work. Despite these risks, we believe that the benefits of releasing LLaVA to the research community outweigh the potential harm. It allows for ongoing investigation and improvement of the model and engages the community in developing better mitigation strategies to address these concerns. Moreover, the release of LLaVA can spur the development of new applications and research directions, ultimately contributing to the progress and responsible deployment of foundation models in vision-language tasks. More Results  We present more qualitative results of LLaVA to analyze its emergent behaviors and observed weaknesses. For more quantitative results of LLaVA on academic benchmarks, please refer to the improved baselines with visual instruction tuning. LLaVA demonstrates a similar behavior as GPT-4 in examples from its paper. Similar to the GPT-4 live demo by OpenAI, LLaVA is capable of generating the HTML/JS/CSS code for an interactive joke website based on a simplified user input sketch, despite a minor error. LLaVA can follow user's instructions in a conversational style and provide detailed responses or creative writings. Furthermore, LLaVA is able to relate the visual content to the textual knowledge from the pretrained LLM. One interesting emergent behavior of LLaVA is that it is able to understand visual contents that are not covered in the training. For example, it is able to recognize Elon Musk both in a headshot and in a humorous meme where he is dressed as a doge, even though Elon Musk never appears in the training data for either the visual feature alignment or visual instruction tuning stages of LLaVA.
As the number of training tokens increases, the model performance improves; however, performance on vision question answering (VQA) tasks exhibits some fluctuation. In contrast, for tasks such as AI2D and InfoVQA--both of which involve understanding textual and graphical information in images--the model performance shows steady improvement as training data is augmented. Conclusion We have presented the Qwen2-VL series, the versatile large vision-language models, including three open-weight models with total parameter counts of 2, 8, and 72 billion. Qwen2-VL matches the performance of top-tier models like GPT-4o and Claude3.5-Sonnet in a range of multimodal scenarios, surpassing all other open-weight LVLM models. Qwen2-VL series introduces naive dynamic resolution and multimodal rotary position embedding (M-RoPE) to fuse information across modals effectively and be capable of understanding videos over 20 minutes in length. With advanced reasoning and decision-making abilities, Qwen2-VL can be integrated with devices such as mobile phones, robots, etc. Furthermore, Qwen2-VL now supports understanding multilingual texts within images, including most European languages, Japanese, Korean, Arabic, Vietnamese, and others. We have made the Qwen2-VL model weights openly accessible, which enables researchers and developers to harness the full potential in a variety of applications and research projects. We aim to advance AI technologies and enhance their beneficial effects on society by dedicating ourselves to these endeavors. Acknowledgements We express our gratitude to Juan Zhu, Fan Hong, Jie Zhang, Yong Li of Alibaba Cloud’s PAI team for supporting the training infrastructure of Qwen2-VL. This work was also supported by Qwen LLM team, and we especially thank Na Ni, Yichang Zhang, Jianxin Ma, Bowen Yu, Zheren Fu for their data contribution and insightful discussion. Appendix Model Capabilities and Qualitative Examples In this section, we present some practical examples of our Qwen2-VL. General Chat and OCR The Qwen2-VL models are now more adept at accurately describing and identifying complex information within images, as well as providing detailed background and answering related questions. Besides, the text processing capabilities of the Qwen2-VL models have seen significant improvements, particularly concerning the recognition of Chinese and English text within images. Visual Agent Capability The Qwen2-VL also excels in location and agent tasks. models have seen significant improvements, particularly concerning the recognition of Chinese and English text within images. Visual Agent Capability The Qwen2-VL also excels in location and agent tasks.