Inventor John Tse has gone high-tech to keep raindrops from falling on your head. Breakthroughs, discoveries, and DIY tips sent six days a week. You wouldn't think it, but for years people have looked at the humble umbrella and seen more than just a way to keep dry during a rainstorm. They see it as a challenge. Are there ways to use it we never thought of before?

The advent of multimodal deep learning models, such as CLIP, has unlocked new frontiers in a wide range of applications, from image-text understanding to classification tasks. However, these models are not safe for adversarial attacks, particularly backdoor attacks, which can subtly manipulate model behavior. Moreover, existing defense methods typically involve training from scratch or fine-tuning using a large dataset without pinpointing the specific labels that are affected. In this study, we introduce an innovative strategy to enhance the robustness of multimodal contrastive learning models against such attacks. In particular, given a poisoned CLIP model, our approach can identify the backdoor trigger and pinpoint the victim samples and labels in an efficient manner. To that end, an image segmentation ``oracle'' is introduced as the supervisor for the output of the poisoned CLIP. We develop two algorithms to rectify the poisoned model: (1) differentiating between CLIP and Oracle's knowledge to identify potential triggers; (2) pinpointing affected labels and victim samples, and curating a compact fine-tuning dataset. With this knowledge, we are allowed to rectify the poisoned CLIP model to negate backdoor effects. Extensive experiments on visual recognition benchmarks demonstrate our strategy is effective in CLIP-based backdoor defense.

We expand Eq. 10 as: q There are three KL divergence terms in our training objective ELBO (Eq. Medium and Y elp Large datasets, we follow (Guu et al., 2018) to use a three-layer attentional LSTM Skip connections are also used between adjacent LSTM layers. We apply annealing and free-bits techniques following (Li et al., 2019) to the KL term on prototype variable, As in Section 4.3, here we show more generated examples through interpolation on MSCOCO dataset. Table 6: Qualitative examples from the MSCOCO dataset on interpolated sentence generation given the prototype.

The'Simprella' weighs less than a few Snickers' bars. Breakthroughs, discoveries, and DIY tips sent every weekday. If you're anything like this author, you've probably burned through your fair share of umbrellas over the years. Large, cumbersome, and often prone to breaking in the wind, umbrellas are one of those necessities of life that, more often than not, end up creating a bigger annoyance than the problem they're meant to solve. A startup called Simp Design is trying to do just that with its new iPhone-sized " Simprella ."

We expand Eq. 10 as: q There are three KL divergence terms in our training objective ELBO (Eq. Medium and Y elp Large datasets, we follow (Guu et al., 2018) to use a three-layer attentional LSTM Skip connections are also used between adjacent LSTM layers. We apply annealing and free-bits techniques following (Li et al., 2019) to the KL term on prototype variable, As in Section 4.3, here we show more generated examples through interpolation on MSCOCO dataset. Table 6: Qualitative examples from the MSCOCO dataset on interpolated sentence generation given the prototype.

In this section, we provide and expand upon a toy example. Suppose that the regulatory guideline requires that users in the same geographical location receive similar weather forecasts. This can be written as "the weather forecasts that are The question of how to quantify "similarity" is addressed in The toy example in Example 5 is illustrated in the right-most panel. Figure 2: Understanding the role of the MVUE (see Section 4.2). Figure 2 visualizes the intuition behind the MVUE discussed in Section 4.2. In this toy example, we study three hypothetical users.)

Breakthroughs, discoveries, and DIY tips sent every weekday. No responsibilities and little to do but enjoy yourself. Yet somehow, after a whole day of blissful nothing, you find yourself completely zonked. If taking in the sea air is supposed to be restorative, why can a restful day at the beach end up feeling so tiring? There's no one certain answer, but science offers a few possibilities.